phd math data science

Ph.D. Specialization in Data Science

The ph.d. specialization in data science is an option within the applied mathematics, computer science, electrical engineering, industrial engineering and operations research, and statistics departments..

Only students already enrolled in one of these doctoral programs at Columbia are eligible to participate in this specialization. Students should fulfill the requirements below in addition to those of their respective department's Ph.D. program. Students should discuss this specialization option with their Ph.D. advisor and their department's director for graduate studies.

Applied Mathematics Doctoral Program

Computer Science Doctoral Program

Decision, Risk, and Operations (DRO) Program

Electrical Engineering Doctoral Program

Industrial Engineering and Operations Research Doctoral Program

Statistics Doctoral Program

The specialization consists of either five (5) courses from the lists below, or four (4) courses plus one (1) additional course approved by the curriculum committee. All courses must be taken for a letter grade and students must pass with a B+ or above. At least three (3) of the courses should come from outside the student’s home department. At least one (1) course has to come from each of the three (3) thematic areas listed below.

Specialization Requirements

• COMS 4231 Analysis of Algorithms I
• COMS 6232 Analysis of Algorithms II
• COMS 4111 Introduction to Databases
• COMS 4113 Distributed Systems Fundamentals
• EECS 6720 Bayesian Models for Machine Learning
• COMS 4771 Machine Learning
• COMS 4772 Advanced Machine Learning
• IEOR E6613 Optimization I
• IEOR E6614 Optimization II
• IEOR E6711 Stochastic Modeling I
• EEOR E6616 Convex Optimization
• STAT 6301 Probability Theory I
• STAT 6201 Theoretical Statistics I
• STAT 6101 Applied Statistics I
• STAT 6104 Computational Statistics
• STAT 5224 Bayesian Statistics
• STCS 6701 Foundations of Graphical Models (joint with Computer Science) 

Information Request Form

Ph.d. specialization committee.

• View All People
• Faculty of Arts and Sciences Professor of Statistics
• The Fu Foundation School of Engineering and Applied Science Professor of Computer Science

Richard A. Davis

• Faculty of Arts and Sciences Howard Levene Professor of Statistics

Vineet Goyal

• The Fu Foundation School of Engineering and Applied Science Associate Professor of Industrial Engineering and Operations Research

Garud N. Iyengar

• Data Science Institute Avanessians Director of the Data Science Institute
• The Fu Foundation School of Engineering and Applied Science Professor of Industrial Engineering and Operations Research

Gail Kaiser

Rocco a. servedio, clifford stein.

• The Fu Foundation School of Engineering and Applied Science Wai T. Chang Professor of Industrial Engineering and Operations Research and Professor of Computer Science

John Wright

• The Fu Foundation School of Engineering and Applied Science Associate Professor of Electrical Engineering
• Data Science Institute Associate Director for Research

Natural Sciences and Mathematics

Mathematical sciences.

Doctor of Philosophy in Data Science and Statistics

The program offers extensive coursework and intensive research experience in theory, methodology, and applications of statistics (see  degree requirements ). 

• Faculty members with broad and diverse research interests are available to supervise doctoral dissertations .
• Financial support in the form of assistantships, full tuition support, and scholarships and awards are provided. Additional scholarships are available for US citizens and permanent residents.
• Our students, both domestic and international, have a strong record of starting in full-time jobs right after graduation .
• Students have opportunities to participate in active  Statistics Seminar  series and the departmental  Colloquium series.
• To enhance career prospects, students can pursue  Graduate Certificate in Data Science , and possibly use the certificate courses to fulfill the PhD degree elective requirements.
• NSM Career Success Center  is available to support professional development and experiential learning of students.  
• GRE test score is not required for admission.

100% of our 22 PhD graduates since 2020, both domestic and international, secured full-time employment within a few months of receiving their degrees. 

Placement of 2022 & 2023 PhD Graduates

See a more complete list

Assistantships

Graduate Teaching Assistantships are offered to qualified PhD students on a competitive basis. These assistantships include a monthly stipend (currently set at $2,400) along with a full tuition waiver (covering 9 credit hours per term in the Fall and Spring semesters). The assistantship additionally covers the cost of health insurance purchased through the university and most fees. Graduate Research Assistantships for advanced PhD students are also available on some faculty members’ research grants. Typically, assistantship support is provided for five years and encompasses the Summer semester as well.

All admitted students are considered for assistantships; no separate application is necessary. 

Scholarships, Fellowships & Awards

PhD students are additionally supported through the following awards:

• NSM McDermott PhD Admission Fellowship  (for highly qualified new students, offered at the time of admission)
• Dean’s Fellowship  and  EEF Scholarship  (for highly qualified new students who are U.S. citizens and permanent residents, offered at the time of admission)
• Julia Williams Van Ness Merit Scholarship  and  Mei Lein Fellowship
• Outstanding Teaching Assistant of the Year Award
• Dean of Graduate Education Dissertation Research Award
• Best Dissertation Award ,  David Daniel Thesis Award , and  Outstanding Graduate Student Award

Conference Travel Support

NSM Conference Travel Award  and  Betty and Gifford Johnson Travel Award  are available to provide financial support to PhD students to present their research at professional conferences.

• How To Apply
• Frequently Asked Questions
• Scholarships & Awards
• Office of Admissions and Enrollment

Graduate Resources

• Mathematics Research
• Statistics & Actuarial Science Research
• Graduate Advisors
• Mathematics Courses
• Statistics Courses
• Actuarial Science Courses
• Qualifying Exam Archive
• Office of Graduate Education

Ready to start your application?

Before you apply, visit our  How to Apply  page to get familiar with the admission requirements and application process.

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Where To Earn A Ph.D. In Data Science Online In 2024

Updated: Apr 3, 2024, 2:15pm

Data science is among the most in-demand skill sets in the modern economy. Data science professionals help businesses make decisions by creating analytical models, combining elements of math, artificial intelligence, machine learning and statistics.

If you want to pursue a high-paying data science career or teach data science at the college level, you may want to earn a terminal degree in the field. Online Ph.D. in data science programs allow you to advance your career while balancing other responsibilities at work or home.

We found two online data science programs that met our ranking criteria. Read on to learn more about these schools and find answers to frequently asked questions about data science.

Why You Can Trust Forbes Advisor Education

Forbes Advisor’s education editors are committed to producing unbiased rankings and informative articles covering online colleges, tech bootcamps and career paths. Our ranking methodologies use data from the National Center for Education Statistics , education providers, and reputable educational and professional organizations. An advisory board of educators and other subject matter experts reviews and verifies our content to bring you trustworthy, up-to-date information. Advertisers do not influence our rankings or editorial content.

• 6,290 accredited, nonprofit colleges and universities analyzed nationwide
• 52 reputable tech bootcamp providers evaluated for our rankings
• All content is fact-checked and updated on an annual basis
• Rankings undergo five rounds of fact-checking
• Only 7.12% of all colleges, universities and bootcamp providers we consider are awarded

Online Ph.D. in Data Science Option

Capitol technology university, national university.

Located just outside Washington, D.C., in South Laurel, Maryland, Capitol Technology University offers an online doctoral degree in business analytics and data science. The program includes a limited residency requirement: Students must complete a course in contemporary research in management on campus, during which they take a qualifying exam. The degree requires 54 to 66 credits, and students can graduate within three years.

All students must also complete a dissertation and an oral defense of their work. The program costs $950 per credit for both in-state and out-of-state learners. Retired and active duty military receive a tuition discount.

At a Glance

• School Type: Private
• Application Fee: $100
• Degree Credit Requirements: 54 to 66 credits
• Program Enrollment Options: Part-time
• Notable Major-Specific Courses: Management theory in a global economy; analytics and decision analysis
• Concentrations Available: N/A
• In-Person Requirements: Yes, for residency

Based in San Diego, California, National University (NU) offers a variety of online programs, including a Ph.D. in data science. NU’s program requires 60 credits and takes an estimated 40 months. NU aims for flexibility, delivering coursework asynchronously and offering a new start date each Monday. The curriculum comprises 20 courses covering data science principles and data preparation methods.

NU runs on the quarter system and charges $442 per quarter unit for graduate courses. The program does not include any in-person requirements.

• Application Fee: Free
• Degree Credit Requirements: 60 credits
• Notable Major-Specific Courses: Principles of data science, data preparation methods
• In-Person Requirements: No

How To Find the Right Online Ph.D. in Data Science for You

Consider your future goals.

A Ph.D. in data science makes sense if you want to become a college professor , conduct original research or compete for the highest-paying and most cognitively demanding business analytics and machine learning positions. If you plan to pursue other careers, you may not need a terminal degree in this field.

If you want to work in academia, make sure your chosen doctorate in data science includes a dissertation requirement. A dissertation allows you to perform original research and contribute to scholarship in your field before you graduate. In turn, you’ll get a sense of your chosen career and a head start on professional publication.

Understand Your Expenses and Financing Options

Per-credit tuition rates for the programs in our guide ranged from $442 to $950. A 60-credit degree from NU totals about $26,500, while the 66-credit option at Capitol Tech costs more than $62,000.

Private universities, including NU and Capitol Tech, tend to cost more than public schools. Graduate students at nonprofit private universities paid an average of $20,408 per year in 2022-23, according to the National Center for Education Statistics . Over the course of a typical three-year Ph.D. program, this translates to about $61,000. This roughly matches Capitol Tech’s tuition, while NU offers a more affordable program.

While a Ph.D. might help you land a lucrative role in the long run, the upfront investment is still significant. Make sure to fill out the FAFSA ® to access federal student aid. This application is the gateway to opportunities like scholarships, grants and loans. You can pursue similar opportunities through schools and nonprofit organizations.

As a doctoral student, you may be able to access graduate assistantships or stipends, but these are often reserved for on-campus students who teach undergraduates or assist professors with research.

Should You Enroll in a Ph.D. in Data Science Online?

Pursuing a Ph.D. in data science online suits a specific kind of learner. To decide if that’s you, ask yourself a few key questions:

• What’s my budget? In some cases, public universities allow students who exclusively enroll in online courses to pay in-state or otherwise discounted tuition rates. Even if you have to pay full price, distance learners generally save on costs associated with housing and transportation.
• What are my other commitments? Distance learning is often a good fit for parents and students who need to work full time while pursuing their degree. Learners with outside responsibilities might pursue a program with asynchronous course delivery, which eliminates scheduled class sessions.
• What’s my learning style? Distance learning requires a great deal of discipline, organization and time management. If you need external accountability or prefer the structure of a peer group or physical classroom, on-campus learning might offer a better fit.

Accreditation for Online Ph.D.s in Data Science

There are two important types of college accreditation to consider: institutional and programmatic.

Institutional accreditation is essential; it involves vetting schools to ensure the quality of their finances, academics, and faculty, among other areas. The Council for Higher Education Accreditation (CHEA) and U.S. Department of Education oversee the regional agencies that administer this process.

You should only enroll at institutionally accredited schools. Otherwise, you will be ineligible for federal financial aid. You can check a school’s accreditation status on its website or by visiting the directory on CHEA’s website .

Individual departments and degrees earn programmatic accreditation based on their curriculum, faculty and learner outcomes. However, this process has not been widely established for data science programs, so it shouldn’t make or break your enrollment decision. However, you can still keep an eye out for accreditation from the Data Science Council of America (DASCA).

Our Methodology

We ranked two accredited, nonprofit colleges offering online Ph.D.s in data science in the U.S. using 15 data points in the categories of student experience, credibility, student outcomes and affordability. We pulled data for these categories from reliable resources such as the Integrated Postsecondary Education Data System ; private, third-party data sources; and individual school and program websites.

Data is accurate as of February 2024. Note that because online doctorates are relatively uncommon, fewer schools meet our ranking standards at the doctoral level.

We scored schools based on the following metrics:

Student Experience:

• Student-to-faculty ratio
• Socioeconomic diversity
• Availability of online coursework
• Total number of graduate assistants
• Proportion of graduate students enrolled in at least some distance education

Credibility:

• Fully accredited
• Programmatic accreditation status
• Nonprofit status

Student Outcomes:

• Overall graduation rate
• Median earnings 10 years after graduation

Affordability:

• In-state graduate student tuition
• In-state graduate student fees
• Alternative tuition plans offered
• Median federal student loan debt
• Student loan default rate

We listed the two schools in the U.S. that met our ranking criteria.

Find our full list of methodologies here .

Frequently Asked Questions (FAQs) About Earning a Ph.D. in Data Science Online

Can i do a ph.d. in data science online.

Yes, you can. National University and Capitol Technology University both offer Ph.D. programs in data science that you can complete mostly or entirely online.

Is a Ph.D. worth it for data science?

It depends on your goals and circumstances. A Ph.D. in data science may be a good fit if you want to pursue a career in research or academia or compete for advanced, lucrative positions in business analytics, artificial intelligence or machine learning.

Is it okay to get a Ph.D. online?

Yes, as long as the program is accredited. Distance learning requires strong motivation and self-discipline, so it suits some students better than others.

Can you become a professor with an online Ph.D.?

Yes, you can. Online diplomas feature the same coursework and degree requirements as in-person degrees, and your degree won’t say “online”.

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PhD in Data Science – Your Guide to Choosing a Doctorate Degree Program

Created by aasif.faizal

Professional opportunities in data science are growing incredibly fast. That’s great news for students looking to pursue a career as a data scientist. But it also means that there are a lot more options out there to investigate and understand before developing the best educational path for you.

A PhD is the most advanced data science degree you can get, reflecting a depth of knowledge and technical expertise that will put you at the top of your field.

This means that PhD programs are the most time-intensive degree option out there, typically requiring that students complete dissertations involving rigorous research. This means that PhDs are not for everyone. Indeed, many who work in the world of big data hold master’s degrees rather than PhDs, which tend to involve the same coursework as PhD programs without a dissertation component. However, for the right candidate, a PhD program is the perfect choice to become a true expert on your area of focus.

If you’ve concluded that a data science PhD is the right path for you, this guide is intended to help you choose the best program to suit your needs. It will walk through some of the key considerations while picking graduate data science programs and some of the nuts and bolts (like course load and tuition costs) that are part of the data science PhD decision-making process.

Data Science PhD vs. Masters: Choosing the right option for you

If you’re considering pursuing a data science PhD, it’s worth knowing that such an advanced degree isn’t strictly necessary in order to get good work opportunities. Many who work in the field of big data only hold master’s degrees, which is the level of education expected to be a competitive candidate for data science positions.

So why pursue a data science PhD?

Simply put, a PhD in data science will leave you qualified to enter the big data industry at a high level from the outset.

You’ll be eligible for advanced positions within companies, holding greater responsibilities, keeping more direct communication with leadership, and having more influence on important data-driven decisions. You’re also likely to receive greater compensation to match your rank.

However, PhDs are not for everyone. Dissertations require a great deal of time and an interest in intensive research. If you are eager to jumpstart a career quickly, a master’s program will give you the preparation you need to hit the ground running. PhDs are appropriate for those who want to commit their time and effort to schooling as a long-term investment in their professional trajectory.

For more information on the difference between data science PhD’s and master’s programs, take a look at our guide here.

Topics include:

• Can I get an Online Ph.D in Data Science?
• Overview of Ph.d Coursework

Preparing for a Doctorate Program

Building a solid track record of professional experience, things to consider when choosing a school.

• What Does it Cost to Get a Ph.D in Data Science?
• School Listings

Data Science PhD Programs, Historically

Historically, data science PhD programs were one of the main avenues to get a good data-related position in academia or industry. But, PhD programs are heavily research oriented and require a somewhat long term investment of time, money, and energy to obtain. The issue that some data science PhD holders are reporting, especially in industry settings, is that that the state of the art is moving so quickly, and that the data science industry is evolving so rapidly, that an abundance of research oriented expertise is not always what’s heavily sought after.

Instead, many companies are looking for candidates who are up to date with the latest data science techniques and technologies, and are willing to pivot to match emerging trends and practices.

One recent development that is making the data science graduate school decisions more complex is the introduction of specialty master’s degrees, that focus on rigorous but compact, professional training. Both students and companies are realizing the value of an intensive, more industry-focused degree that can provide sufficient enough training to manage complex projects and that are more client oriented, opposed to research oriented.

However, not all prospective data science PhD students are looking for jobs in industry. There are some pretty amazing research opportunities opening up across a variety of academic fields that are making use of new data collection and analysis tools. Experts that understand how to leverage data systems including statistics and computer science to analyze trends and build models will be in high demand.

Can You Get a PhD in Data Science Online?

While it is not common to get a data science Ph.D. online, there are currently two options for those looking to take advantage of the flexibility of an online program.

Indiana University Bloomington and Northcentral University both offer online Ph.D. programs with either a minor or specialization in data science.

Given the trend for schools to continue increasing online offerings, expect to see additional schools adding this option in the near future.

Overview of PhD Coursework

A PhD requires a lot of academic work, which generally requires between four and five years (sometimes longer) to complete.

Here are some of the high level factors to consider and evaluate when comparing data science graduate programs.

How many credits are required for a PhD in data science?

On average, it takes 71 credits to graduate with a PhD in data science — far longer (almost double) than traditional master’s degree programs. In addition to coursework, most PhD students also have research and teaching responsibilities that can be simultaneously demanding and really great career preparation.

What’s the core curriculum like?

In a data science doctoral program, you’ll be expected to learn many skills and also how to apply them across domains and disciplines. Core curriculums will vary from program to program, but almost all will have a core foundation of statistics.

All PhD candidates will have to take a qualifying exam. This can vary from university to university, but to give you some insight, it is broken up into three phases at Yale. They have a practical exam, a theory exam and an oral exam. The goal is to make sure doctoral students are developing the appropriate level of expertise.

Dissertation

One of the final steps of a PhD program involves presenting original research findings in a formal document called a dissertation. These will provide background and context, as well as findings and analysis, and can contribute to the understanding and evolution of data science. A dissertation idea most often provides the framework for how a PhD candidate’s graduate school experience will unfold, so it’s important to be thoughtful and deliberate while considering research opportunities.

Since data science is such a rapidly evolving field and because choosing the right PhD program is such an important factor in developing a successful career path, there are some steps that prospective doctoral students can take in advance to find the best-fitting opportunity.

Join professional associations

Even before being fully credentials, joining professional associations and organizations such as the Data Science Association and the American Association of Big Data Professionals is a good way to get exposure to the field. Many professional societies are welcoming to new members and even encourage student participation with things like discounted membership fees and awards and contest categories for student researchers. One of the biggest advantages to joining is that these professional associations bring together other data scientists for conference events, research-sharing opportunities, networking and continuing education opportunities.

Leverage your social network

Be on the lookout to make professional connections with professors, peers, and members of industry. There are a number of LinkedIn groups dedicated to data science. A well-maintained professional network is always useful to have when looking for advice or letters of recommendation while applying to graduate school and then later while applying for jobs and other career-related opportunities.

Kaggle competitions

Kaggle competitions provide the opportunity to solve real-world data science problems and win prizes. A list of data science problems can be found at Kaggle.com . Winning one of these competitions is a good way to demonstrate professional interest and experience.

Internships

Internships are a great way to get real-world experience in data science while also getting to work for top names in the world of business. For example, IBM offers a data science internship which would also help to stand out when applying for PhD programs, as well as in seeking employment in the future.

Demonstrating professional experience is not only important when looking for jobs, but it can also help while applying for graduate school. There are a number of ways for prospective students to gain exposure to the field and explore different facets of data science careers.

Get certified

There are a number of data-related certificate programs that are open to people with a variety of academic and professional experience. DeZyre has an excellent guide to different certifications, some of which might help provide good background for graduate school applications.

Conferences

Conferences are a great place to meet people presenting new and exciting research in the data science field and bounce ideas off of newfound connections. Like professional societies and organizations, discounted student rates are available to encourage student participation. In addition, some conferences will waive fees if you are presenting a poster or research at the conference, which is an extra incentive to present.

It can be hard to quantify what makes a good-fit when it comes to data science graduate school programs. There are easy to evaluate factors, such as cost and location, and then there are harder to evaluate criteria such as networking opportunities, accessibility to professors, and the up-to-dateness of the program’s curriculum.

Nevertheless, there are some key relevant considerations when applying to almost any data science graduate program.

What most schools will require when applying:

• All undergraduate and graduate transcripts
• A statement of intent for the program (reason for applying and future plans)
• Letters of reference
• Application fee
• Online application
• A curriculum vitae (outlining all of your academic and professional accomplishments)

What Does it Cost to Get a PhD in Data Science?

The great news is that many PhD data science programs are supported by fellowships and stipends. Some are completely funded, meaning the school will pay tuition and basic living expenses. Here are several examples of fully funded programs:

• University of Southern California
• University of Nevada, Reno
• Kennesaw State University
• Worcester Polytechnic Institute
• University of Maryland

For all other programs, the average range of tuition, depending on the school can range anywhere from $1,300 per credit hour to $2,000 amount per credit hour. Remember, typical PhD programs in data science are between 60 and 75 credit hours, meaning you could spend up to $150,000 over several years.

That’s why the financial aspects are so important to evaluate when assessing PhD programs, because some schools offer full stipends so that you are able to attend without having to find supplemental scholarships or tuition assistance.

Can I become a professor of data science with a PhD.? Yes! If you are interested in teaching at the college or graduate level, a PhD is the degree needed to establish the full expertise expected to be a professor. Some data scientists who hold PhDs start by entering the field of big data and pivot over to teaching after gaining a significant amount of work experience. If you’re driven to teach others or to pursue advanced research in data science, a PhD is the right degree for you.

Do I need a master’s in order to pursue a PhD.? No. Many who pursue PhDs in Data Science do not already hold advanced degrees, and many PhD programs include all the coursework of a master’s program in the first two years of school. For many students, this is the most time-effective option, allowing you to complete your education in a single pass rather than interrupting your studies after your master’s program.

Can I choose to pursue a PhD after already receiving my master’s? Yes. A master’s program can be an opportunity to get the lay of the land and determine the specific career path you’d like to forge in the world of big data. Some schools may allow you to simply extend your academic timeline after receiving your master’s degree, and it is also possible to return to school to receive a PhD if you have been working in the field for some time.

If a PhD. isn’t necessary, is it a waste of time? While not all students are candidates for PhDs, for the right students – who are keen on doing in-depth research, have the time to devote to many years of school, and potentially have an interest in continuing to work in academia – a PhD is a great choice. For more information on this question, take a look at our article Is a Data Science PhD. Worth It?

Complete List of Data Science PhD Programs

Below you will find the most comprehensive list of schools offering a doctorate in data science. Each school listing contains a link to the program specific page, GRE or a master’s degree requirements, and a link to a page with detailed course information.

Note that the listing only contains true data science programs. Other similar programs are often lumped together on other sites, but we have chosen to list programs such as data analytics and business intelligence on a separate section of the website.

Boise State University  – Boise, Idaho PhD in Computing – Data Science Concentration

The Data Science emphasis focuses on the development of mathematical and statistical algorithms, software, and computing systems to extract knowledge or insights from data.  

In 60 credits, students complete an Introduction to Graduate Studies, 12 credits of core courses, 6 credits of data science elective courses, 10 credits of other elective courses, a Doctoral Comprehensive Examination worth 1 credit, and a 30-credit dissertation.

Electives can be taken in focus areas such as Anthropology, Biometry, Ecology/Evolution and Behavior, Econometrics, Electrical Engineering, Earth Dynamics and Informatics, Geoscience, Geostatistics, Hydrology and Hydrogeology, Materials Science, and Transportation Science.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $7,236 total (Resident), $24,573 total (Non-resident)

View Course Offerings

Bowling Green State University  – Bowling Green, Ohio Ph.D. in Data Science

Data Science students at Bowling Green intertwine knowledge of computer science with statistics.

Students learn techniques in analyzing structured, unstructured, and dynamic datasets.

Courses train students to understand the principles of analytic methods and articulating the strengths and limitations of analytical methods.

The program requires 60 credit hours in the studies of Computer Science (6 credit hours), Statistics (6 credit hours), Data Science Exploration and Communication, Ethical Issues, Advanced Data Mining, and Applied Data Science Experience.

Students must also complete 21 credit hours of elective courses, a qualifying exam, a preliminary exam, and a dissertation.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $8,418 (Resident), $14,410 (Non-resident)

Brown University  – Providence, Rhode Island PhD in Computer Science – Concentration in Data Science

Brown University’s database group is a world leader in systems-oriented database research; they seek PhD candidates with strong system-building skills who are interested in researching TupleWare, MLbase, MDCC, Crowd DB, or PIQL.

In order to gain entrance, applicants should consider first doing a research internship at Brown with this group. Other ways to boost an application are to take and do well at massive open online courses, do an internship at a large company, and get involved in a large open-source software project.

Coding well in C++ is preferred.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $62,680 total

Chapman University  – Irvine, California Doctorate in Computational and Data Sciences

Candidates for the doctorate in computational and data science at Chapman University begin by completing 13 core credits in basic methodologies and techniques of computational science.

Students complete 45 credits of electives, which are personalized to match the specific interests and research topics of the student.

Finally, students complete up to 12 credits in dissertation research.

Applicants must have completed courses in differential equations, data structures, and probability and statistics, or take specific foundation courses, before beginning coursework toward the PhD.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $37,538 per year

Clemson University / Medical University of South Carolina (MUSC) – Joint Program – Clemson, South Carolina & Charleston, South Carolina Doctor of Philosophy in Biomedical Data Science and Informatics – Clemson

The PhD in biomedical data science and informatics is a joint program co-authored by Clemson University and the Medical University of South Carolina (MUSC).

Students choose one of three tracks to pursue: precision medicine, population health, and clinical and translational informatics. Students complete 65-68 credit hours, and take courses in each of 5 areas: biomedical informatics foundations and applications; computing/math/statistics/engineering; population health, health systems, and policy; biomedical/medical domain; and lab rotations, seminars, and doctoral research.

Applicants must have a bachelor’s in health science, computing, mathematics, statistics, engineering, or a related field, and it is recommended to also have competency in a second of these areas.

Program requirements include a year of calculus and college biology, as well as experience in computer programming.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $10,858 total (South Carolina Resident), $22,566 total (Non-resident)

View Course Offerings – Clemson

George Mason University  – Fairfax, Virginia Doctor of Philosophy in Computational Sciences and Informatics – Emphasis in Data Science

George Mason’s PhD in computational sciences and informatics requires a minimum of 72 credit hours, though this can be reduced if a student has already completed a master’s. 48 credits are toward graduate coursework, and an additional 24 are for dissertation research.

Students choose an area of emphasis—either computer modeling and simulation or data science—and completed 18 credits of the coursework in this area. Students are expected to completed the coursework in 4-5 years.

Applicants to this program must have a bachelor’s degree in a natural science, mathematics, engineering, or computer science, and must have knowledge and experience with differential equations and computer programming.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $13,426 total (Virginia Resident), $35,377 total (Non-resident)

Harrisburg University of Science and Technology  – Harrisburg, Pennsylvania Doctor of Philosophy in Data Sciences

Harrisburg University’s PhD in data science is a 4-5 year program, the first 2 of which make up the Harrisburg master’s in analytics.

Beyond this, PhD candidates complete six milestones to obtain the degree, including 18 semester hours in doctoral-level courses, such as multivariate data analysis, graph theory, machine learning.

Following the completion of ANLY 760 Doctoral Research Seminar, students in the program complete their 12 hours of dissertation research bringing the total program hours to 36.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $14,940 total

Icahn School of Medicine at Mount Sinai  – New York, New York Genetics and Data Science, PhD

As part of the Biomedical Science PhD program, the Genetics and Data Science multidisciplinary training offers research opportunities that expand on genetic research and modern genomics. The training also integrates several disciplines of biomedical sciences with machine learning, network modeling, and big data analysis.

Students in the Genetics and Data Science program complete a predetermined course schedule with a total of 64 credits and 3 years of study.

Additional course requirements and electives include laboratory rotations, a thesis proposal exam and thesis defense, Computer Systems, Intro to Algorithms, Machine Learning for Biomedical Data Science, Translational Genomics, and Practical Analysis of a Personal Genome.

Delivery Method: Campus GRE: Not Required 2022-2023 Tuition: $31,303 total

Indiana University-Purdue University Indianapolis  – Indianapolis, Indiana PhD in Data Science PhD Minor in Applied Data Science

Doctoral candidates pursuing the PhD in data science at Indiana University-Purdue must display competency in research, data analytics, and at management and infrastructure to earn the degree.

The PhD is comprised of 24 credits of a data science core, 18 credits of methods courses, 18 credits of a specialization, written and oral qualifying exams, and 30 credits of dissertation research. All requirements must be completed within 7 years.

Applicants are generally expected to have a master’s in social science, health, data science, or computer science. 

Currently a majority of the PhD students at IUPUI are funded by faculty grants and two are funded by the federal government. None of the students are self funded.

IUPUI also offers a PhD Minor in Applied Data Science that is 12-18 credits. The minor is open to students enrolled at IUPUI or IU Bloomington in a doctoral program other than Data Science.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $9,228 per year (Indiana Resident), $25,368 per year (Non-resident)

Jackson State University – Jackson, Mississippi PhD Computational and Data-Enabled Science and Engineering

Jackson State University offers a PhD in computational and data-enabled science and engineering with 5 concentration areas: computational biology and bioinformatics, computational science and engineering, computational physical science, computation public health, and computational mathematics and social science.

Students complete 12 credits of common core courses, 12 credits in the specialization, 24 credits of electives, and 24 credits in dissertation research.

Students may complete the doctoral program in as little as 5 years and no more than 8 years.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $8,270 total

Kennesaw State University  – Kennesaw, Georgia PhD in Analytics and Data Science

Students pursuing a PhD in analytics and data science at Kennesaw State University must complete 78 credit hours: 48 course hours and 6 electives (spread over 4 years of study), a minimum 12 credit hours for dissertation research, and a minimum 12 credit-hour internship.

Prior to dissertation research, the comprehensive examination will cover material from the three areas of study: computer science, mathematics, and statistics.

Successful applicants will have a master’s degree in a computational field, calculus I and II, programming experience, modeling experience, and are encouraged to have a base SAS certification.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $5,328 total (Georgia Resident), $19,188 total (Non-resident)

New Jersey Institute of Technology  – Newark, New Jersey PhD in Business Data Science

Students may enter the PhD program in business data science at the New Jersey Institute of Technology with either a relevant bachelor’s or master’s degree. Students with bachelor’s degrees begin with 36 credits of advanced courses, and those with master’s take 18 credits before moving on to credits in dissertation research.

Core courses include business research methods, data mining and analysis, data management system design, statistical computing with SAS and R, and regression analysis.

Students take qualifying examinations at the end of years 1 and 2, and must defend their dissertations successfully by the end of year 6.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $21,932 total (New Jersey Resident), $32,426 total (Non-resident)

New York University  – New York, New York PhD in Data Science

Doctoral candidates in data science at New York University must complete 72 credit hours, pass a comprehensive and qualifying exam, and defend a dissertation with 10 years of entering the program.

Required courses include an introduction to data science, probability and statistics for data science, machine learning and computational statistics, big data, and inference and representation.

Applicants must have an undergraduate or master’s degree in fields such as mathematics, statistics, computer science, engineering, or other scientific disciplines. Experience with calculus, probability, statistics, and computer programming is also required.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $37,332 per year

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Northcentral University  – San Diego, California PhD in Data Science-TIM

Northcentral University offers a PhD in technology and innovation management with a specialization in data science.

The program requires 60 credit hours, including 6-7 core courses, 3 in research, a PhD portfolio, and 4 dissertation courses.

The data science specialization requires 6 courses: data mining, knowledge management, quantitative methods for data analytics and business intelligence, data visualization, predicting the future, and big data integration.

Applicants must have a master’s already.

Delivery Method: Online GRE: Required 2022-2023 Tuition: $16,794 total

Stevens Institute of Technology – Hoboken, New Jersey Ph.D. in Data Science

Stevens Institute of Technology has developed a data science Ph.D. program geared to help graduates become innovators in the space.

The rigorous curriculum emphasizes mathematical and statistical modeling, machine learning, computational systems and data management.

The program is directed by Dr. Ted Stohr, a recognized thought leader in the information systems, operations and business process management arenas.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $39,408 per year

University at Buffalo – Buffalo, New York PhD Computational and Data-Enabled Science and Engineering

The curriculum for the University of Buffalo’s PhD in computational and data-enabled science and engineering centers around three areas: data science, applied mathematics and numerical methods, and high performance and data intensive computing. 9 credit course of courses must be completed in each of these three areas. Altogether, the program consists of 72 credit hours, and should be completed in 4-5 years. A master’s degree is required for admission; courses taken during the master’s may be able to count toward some of the core coursework requirements.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $11,310 per year (New York Resident), $23,100 per year (Non-resident)

University of Colorado Denver – Denver, Colorado PhD in Big Data Science and Engineering

The University of Colorado – Denver offers a unique program for those students who have already received admission to the computer science and information systems PhD program.

The Big Data Science and Engineering (BDSE) program is a PhD fellowship program that allows selected students to pursue research in the area of big data science and engineering. This new fellowship program was created to train more computer scientists in data science application fields such as health informatics, geosciences, precision and personalized medicine, business analytics, and smart cities and cybersecurity.

Students in the doctoral program must complete 30 credit hours of computer science classes beyond a master’s level, and 30 credit hours of dissertation research.

The BDSE fellowship requires students to have an advisor both in the core disciplines (either computer science or mathematics and statistics) as well as an advisor in the application discipline (medicine and public health, business, or geosciences).

In addition, the fellowship covers full stipend, tuition, and fees up to ~50k for BDSE fellows annually. Important eligibility requirements can be found here.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $55,260 total

University of Marylan d  – College Park, Maryland PhD in Information Studies

Data science is a potential research area for doctoral candidates in information studies at the University of Maryland – College Park. This includes big data, data analytics, and data mining.

Applicants for the PhD must have taken the following courses in undergraduate studies: programming languages, data structures, design and analysis of computer algorithms, calculus I and II, and linear algebra.

Students must complete 6 qualifying courses, 2 elective graduate courses, and at least 12 credit hours of dissertation research.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $16,238 total (Maryland Resident), $35,388 total (Non-resident)

University of Massachusetts Boston  – Boston, Massachusetts PhD in Business Administration – Information Systems for Data Science Track

The University of Massachusetts – Boston offers a PhD in information systems for data science. As this is a business degree, students must complete coursework in their first two years with a focus on data for business; for example, taking courses such as business in context: markets, technologies, and societies.

Students must take and pass qualifying exams at the end of year 1, comprehensive exams at the end of year 2, and defend their theses at the end of year 4.

Those with a degree in statistics, economics, math, computer science, management sciences, information systems, and other related fields are especially encouraged, though a quantitative degree is not necessary.

Students accepted by the program are ordinarily offered full tuition credits and a stipend ($25,000 per year) to cover educational expenses and help defray living costs for up to three years of study.

During the first two years of coursework, they are assigned to a faculty member as a research assistant; for the third year students will be engaged in instructional activities. Funding for the fourth year is merit-based from a limited pool of program funds

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $18,894 total (in-state), $36,879 (out-of-state)

University of Nevada Reno – Reno, Nevada PhD in Statistics and Data Science

The University of Nevada – Reno’s doctoral program in statistics and data science is comprised of 72 credit hours to be completed over the course of 4-5 years. Coursework is all within the scope of statistics, with titles such as statistical theory, probability theory, linear models, multivariate analysis, statistical learning, statistical computing, time series analysis.

The completion of a Master’s degree in mathematics or statistics prior to enrollment in the doctoral program is strongly recommended, but not required.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $5,814 total (in-state), $22,356 (out-of-state)

University of Southern California – Los Angles, California PhD in Data Sciences & Operations

USC Marshall School of Business offers a PhD in data sciences and operations to be completed in 5 years.

Students can choose either a track in operations management or in statistics. Both tracks require 4 courses in fall and spring of the first 2 years, as well as a research paper and courses during the summers. Year 3 is devoted to dissertation preparation and year 4 and/or 5 to dissertation defense.

A bachelor’s degree is necessary for application, but no field or further experience is required.

Students should complete 60 units of coursework. If the students are admitted with Advanced Standing (e.g., Master’s Degree in appropriate field), this requirement may be reduced to 40 credits.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $63,468 total

University of Tennessee-Knoxville  – Knoxville, Tennessee The Data Science and Engineering PhD

The data science and engineering PhD at the University of Tennessee – Knoxville requires 36 hours of coursework and 36 hours of dissertation research. For those entering with an MS degree, only 24 hours of course work is required.

The core curriculum includes work in statistics, machine learning, and scripting languages and is enhanced by 6 hours in courses that focus either on policy issues related to data, or technology entrepreneurship.

Students must also choose a knowledge specialization in one of these fields: health and biological sciences, advanced manufacturing, materials science, environmental and climate science, transportation science, national security, urban systems science, and advanced data science.

Applicants must have a bachelor’s or master’s degree in engineering or a scientific field. 

All students that are admitted will be supported by a research fellowship and tuition will be included.

Many students will perform research with scientists from Oak Ridge national lab, which is located about 30 minutes drive from campus.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $11,468 total (Tennessee Resident), $29,656 total (Non-resident)

University of Vermont – Burlington, Vermont Complex Systems and Data Science (CSDS), PhD

Through the College of Engineering and Mathematical Sciences, the Complex Systems and Data Science (CSDS) PhD program is pan-disciplinary and provides computational and theoretical training. Students may customize the program depending on their chosen area of focus.

Students in this program work in research groups across campus.

Core courses include Data Science, Principles of Complex Systems and Modeling Complex Systems. Elective courses include Machine Learning, Complex Networks, Evolutionary Computation, Human/Computer Interaction, and Data Mining.

The program requires at least 75 credits to graduate with approval by the student graduate studies committee.

Delivery Method: Campus GRE: Not Required 2022-2023 Tuition: $12,204 total (Vermont Resident), $30,960 total (Non-resident)

University of Washington Seattle Campus – Seattle, Washington PhD in Big Data and Data Science

The University of Washington’s PhD program in data science has 2 key goals: training of new data scientists and cyberinfrastructure development, i.e., development of open-source tools and services that scientists around the world can use for big data analysis.

Students must take core courses in data management, machine learning, data visualization, and statistics.

Students are also required to complete at least one internship that covers practical work in big data.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $17,004 per year (Washington resident), $30,477 (non-resident)

University of Wisconsin-Madison – Madison, Wisconsin PhD in Biomedical Data Science

The PhD program in Biomedical Data Science offered by the Department of Biostatistics and Medical Informatics at UW-Madison is unique, in blending the best of statistics and computer science, biostatistics and biomedical informatics. 

Students complete three year-long course sequences in biostatistics theory and methods, computer science/informatics, and a specialized sequence to fit their interests.

Students also complete three research rotations within their first two years in the program, to both expand their breadth of knowledge and assist in identifying a research advisor.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $10,728 total (in-state), $24,054 total (out-of-state)

Vanderbilt University – Nashville, Tennessee Data Science Track of the BMI PhD Program

The PhD in biomedical informatics at Vanderbilt has the option of a data science track.

Students complete courses in the areas of biomedical informatics (3 courses), computer science (4 courses), statistical methods (4 courses), and biomedical science (2 courses). Students are expected to complete core courses and defend their dissertations within 5 years of beginning the program.

Applicants must have a bachelor’s degree in computer science, engineering, biology, biochemistry, nursing, mathematics, statistics, physics, information management, or some other health-related field.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $53,160 per year

Washington University in St. Louis – St. Louis, Missouri Doctorate in Computational & Data Sciences

Washington University now offers an interdisciplinary Ph.D. in Computational & Data Sciences where students can choose from one of four tracks (Computational Methodologies, Political Science, Psychological & Brain Sciences, or Social Work & Public Health).

Students are fully funded and will receive a stipend for at least five years contingent on making sufficient progress in the program.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $59,420 total

Worcester Polytechnic Institute – Worcester, Massachusetts PhD in Data Science

The PhD in data science at Worcester Polytechnic Institute focuses on 5 areas: integrative data science, business intelligence and case studies, data access and management, data analytics and mining, and mathematical analysis.

Students first complete a master’s in data science, and then complete 60 credit hours beyond the master’s, including 30 credit hours of research.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $28,980 per year

Yale University – New Haven, Connecticut PhD Program – Department of Stats and Data Science

The PhD in statistics and data science at Yale University offers broad training in the areas of statistical theory, probability theory, stochastic processes, asymptotics, information theory, machine learning, data analysis, statistical computing, and graphical methods. Students complete 12 courses in the first year in these topics.

Students are required to teach one course each semester of their third and fourth years.

Most students complete and defend their dissertations in their fifth year.

Applicants should have an educational background in statistics, with an undergraduate major in statistics, mathematics, computer science, or similar field.

Delivery Method: Campus GRE: Required 2022-2023 Tuition: $46,900 total

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DEPARTMENT OF STATISTICS AND DATA SCIENCE

Phd program, phd program overview.

The doctoral program in Statistics and Data Science is designed to provide students with comprehensive training in theory and methodology in statistics and data science, and their applications to problems in a wide range of fields. The program is flexible and may be arranged to reflect students' interests and career goals. Cross-disciplinary work is encouraged. The PhD program prepares students for careers as university teachers and researchers and as research statisticians or data scientists in industry, government and the non-profit sector.

Requirements

Students are required to fulfill the Department requirements in addition to those specified by The Graduate School (TGS).

From the Graduate School’s webpage outlining the general requirements for a PhD :

In order to receive a doctoral degree, students must:

• Complete all required coursework. .
• Gain admittance to candidacy.
• Submit a prospectus to be approved by a faculty committee.
• Present a dissertation with original research. Review the Dissertation Publication page for more information.
• Complete the necessary teaching requirement
• Submit necessary forms to file for graduation
• Complete degree requirements within the approved timeline

PhD degrees must be approved by the student's academic program. Consult with your program directly regarding specific degree requirements.

The Department requires that students in the Statistics and Data Science PhD program:

• Meet the department minimum residency requirement of 2 years
• STAT 344-0 Statistical Computing
• STAT 350-0 Regression Analysis
• STAT 353-0 Advanced Regression (new 2021-22)
• STAT 415-0 I ntroduction to Machine Learning
• STAT 420-1,2,3 Introduction to Statistical Theory and Methodology 1, 2, 3
• STAT 430-1, STAT 430-2, STAT 440 (new courses in 2022-23 on probability and stochastic processes for statistics students)
• STAT 457-0 Applied Bayesian Inference

Students generally complete the required coursework during their first two years in the PhD program. *note that required courses changed in the 2021-22 academic year, previous required courses can be found at the end of this page.

• Pass the Qualifying Exam. This comprehensive examination covers basic topics in statistics and is typically taken in fall quarter of the second year.

Pass the Prospectus presentation/examination and be admitted for PhD candidacy by the end of year 3 . The statistics department requires that students must complete their Prospectus (proposal of dissertation topic) before the end of year 3, which is earlier than The Graduate School deadline of the end of year 4. The prospectus must be approved by a faculty committee comprised of a committee chair and a minimum of 2 other faculty members. Students usually first find an adviser through independent studies who will then typically serve as the committee chair. When necessary, exceptions may be made upon the approval of the committee chair and the director of graduate studies, to extend the due date of the prospectus exam until the end of year 4.

• Successfully complete and defend a doctoral dissertation. After the prospectus is approved, students begin work on the doctoral dissertation, which must demonstrate an original contribution to a chosen area of specialization. A final examination (thesis defense) is given based on the dissertation. Students typically complete the PhD program in 5 years.
• Attend all seminars in the department and participate in other research activities . In addition to these academic requirements, students are expected to participate in other research activities and attend all department seminars every year they are in the program.

Optional MS degree en route to PhD

Students admitted to the Statistics and Data Science PhD program can obtain an optional MS (Master of Science) degree en route to their PhD. The MS degree requires 12 courses: STAT 350-0 Regression Analysis, STAT 353 Advanced Regression, STAT 420-1,2,3 Introduction to Statistical Theory and Methodology 1, 2, 3, STAT 415-0 I ntroduction to Machine Learning , and at least 6 more courses approved by the department of which two must be 400 level STAT elective courses, no more than 3 can be non-STAT courses. For the optional MS degree, students must also pass the qualifying exam offered at the beginning of the second year at the MS level.

*Prior to 2021-2022, the course requirements for the PhD were:

• STAT 351-0 Design and Analysis of Experiments
• STAT 425 Sampling Theory and Applications
• MATH 450-1,2 Probability 1, 2 or MATH 450-1 Probability 1 and IEMS 460-1,2 Stochastic Processes 1, 2
• Six additional 300/400 graduate-level Statistics courses, at least two must be 400 -level

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PhD Program

Wharton’s PhD program in Statistics provides the foundational education that allows students to engage both cutting-edge theory and applied problems. These include problems from a wide variety of fields within Wharton, such as finance, marketing, and public policy, as well as fields across the rest of the University such as biostatistics within the Medical School and computer science within the Engineering School.

Major areas of departmental research include: analysis of observational studies; Bayesian inference, bioinformatics; decision theory; game theory; high dimensional inference; information theory; machine learning; model selection; nonparametric function estimation; and time series analysis.

Students typically have a strong undergraduate background in mathematics. Knowledge of linear algebra and advanced calculus is required, and experience with real analysis is helpful. Although some exposure to undergraduate probability and statistics is expected, skills in mathematics and computer science are more important. Graduates of the department typically take positions in academia, government, financial services, and bio-pharmaceutical industries.

Apply online here .

Department of Statistics and Data Science

The Wharton School, University of Pennsylvania Academic Research Building 265 South 37th Street, 3rd & 4th Floors Philadelphia, PA 19104-1686

Phone: (215) 898-8222

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Ph.D. Program

Advising The vice chair for graduate studies is the chief graduate adviser and heads a committee of faculty advisers who may serve as academic advisers. The research interests of the members of this committee span most of the major areas of statistics. During their first quarter in the program students are required to meet with an academic adviser who assists them in planning a reasonable course of study. In addition, the academic adviser is responsible for monitoring the student’s degree progress and approving the study list each quarter. Students are encouraged to begin thinking about their research interests as early as possible. After the student identifies a dissertation topic, the chair of the dissertation committee becomes the student’s academic adviser.

Continuing students should meet with either the vice chair for graduate studies or their academic adviser at least once each quarter and a record of this interview is placed in the student’s academic file. Each fall a committee consisting of all regular departmental faculty meet to evaluate the progress of all enrolled doctoral students. This committee decides if students are making satisfactory progress, and if not offers specific recommendations to correct the situation. For students who have begun dissertation work, the determination of satisfactory progress is typically delegated to the academic adviser. Students who are found to be consistently performing unsatisfactorily may be recommended for termination by a vote of this committee. Doctoral students normally are considered to be making satisfactory progress if they take the written qualifying examination in the summer following their first year of study and the University Oral Qualifying Examination by the end of their second year.

Major Fields or Sub-disciplines The strengths of current and prospective faculty dictate the specific fields of emphasis in the department: applied multivariate analysis; bioinformatics ( Center for Statistical Research in Computational Biology ); computational and computer-intensive statistics; computer vision; cognition; artificial intelligence; machine learning ( Center for Vision, Cognition, Learning, and Autonomy ); social statistics ( Center for Social Statistics ); experimental design and environmental statistics.

Foreign Language Requirement None.

Course Requirements Students are required to pass, with a grade of B- or better, 54 units of approved graduate course work (200 series) and to maintain an overall grade-point average of 3.0 or better. At least 40 of these units must be in courses from this department; the remaining units may be from courses in related departments. Students are strongly encouraged to take Statistics 200A-200B-200C, 201A-201B-201C, and 202A-202B-202C. All doctoral students are required to take Statistics 290 for at least six quarters, and strongly encouraged to take Stats 290 during each quarter of enrollment. In addition, all doctoral students can take Statistics 296 and/or 596, or 599 as needed. Please note that up to two units of Statistics 285 and eight units of Statistics 596 can be counted toward the 40 units from our department. Stats 290, 296, and 599 are not counted.

Students with gaps in their previous training are allowed to take, with the approval of their academic adviser, undergraduate courses offered by the department. However, Statistics 100A-100B-100C, 101A-101B-101C and 102A-102B-102C may not be applied toward course requirements for a graduate degree in the department. Students who need a basic refresher course are encouraged to take Statistics 100A-100B-100C.

Teaching Experience Students are required to complete at least one quarter of service as a teaching assistant for a minimum of 25% time appointment. Students who serve as teaching assistants in the department must have taken or be currently enrolled in Statistics 495A-495B-495C. International students for whom English is a second language must pass either the Test of Spoken English (TSE) or the UCLA Test of Oral Proficiency (TOP) in English before they may serve as teaching assistants.

Written and Oral Qualifying Examinations Academic Senate regulations require all doctoral students to complete and pass university written and oral qualifying examinations prior to doctoral advancement to candidacy. Also, under Senate regulations, the University Oral Qualifying Examination is open only to the student and appointed members of the doctoral committee. In addition to university requirements, some graduate programs have other pre-candidacy examination requirements. What follows in this section is how students are required to fulfill all of these requirements for this doctoral program.

All committee nominations and reconstitutions adhere to the Minimum Standards for Doctoral Committee Constitution.

The written qualifying examination consists of a high-quality paper, solely authorized by the student. This paper can be a research paper containing an original contribution, or a focused critical survey paper. The paper should demonstrate that the student understands and can integrate and communicate ideas clearly and concisely. The paper should be approximately 10 pages, single-spaced, and the style should be suitable for submission to a first-rate journal or technical conference. Any contributions that are not the student’s, including those of the student’s adviser, must be explicitly acknowledged in detail.

After passing the written qualifying examination, students select a doctoral committee that administers the University Oral Qualifying Examination, required for advancement to candidacy. Students are encouraged to begin thinking about their research interests as early as possible and to seek out faculty members who might serve on their doctoral committee. Students making satisfactory progress are expected to take the written qualifying examination in the summer following their first year of study and the University Oral Qualifying Examination by the end of their second year.

Advancement to Candidacy Students are advanced to candidacy and awarded the Candidate in Philosophy (C.Phil.) degree upon successful completion of the written and oral qualifying examinations.

Doctoral Dissertation Every doctoral degree program requires the completion of an approved dissertation that demonstrates the student’s ability to perform original, independent research and constitutes a distinct contribution to knowledge in the principal field of study.

Final Oral Examination (Defense of the Dissertation) Required for all students in the program. Please see the Advice on Taking the Oral Exam for more information.

Time-to-Degree Students are expected to advance to candidacy for the Ph.D. degree within six quarters of full-time work. Completion of all degree requirements (including the dissertation) normally takes 15 quarters. The maximum time to degree is 24 quarters.

Termination of Graduate Study and Appeal of Termination

University Policy

A student who fails to meet the above requirements may be recommended for termination of graduate study. A graduate student may be disqualified from continuing in the graduate program for a variety of reasons. The most common is failure to maintain the minimum cumulative grade point average (3.00) required by the Academic Senate to remain in good standing (some programs require a higher grade point average). Other examples include failure of examinations, lack of timely progress toward the degree and poor performance in core courses. Probationary students (those with cumulative grade point averages below 3.00) are subject to immediate dismissal upon the recommendation of their department. University guidelines governing termination of graduate students, including the appeal procedure, are outlined in Standards and Procedures for Graduate Study at UCLA.

Special Departmental or Program Policy for the Ph.D. Program

A student who does not advance to doctoral candidacy within six quarters of full-time study is subject to a recommendation for termination. The graduate vice chair informs a student of such a recommendation and the student is asked to submit a written appeal and to solicit letters of support from members of the faculty. The appeal is considered by the Graduate Studies Committee, which makes the final departmental decision.

For Students Who Entered Before Fall 2022 Please click this link . Then navigate to “Program Requirements” in the tab that opens and select the academic year when you matriculated.

Timeline to Filing Your Dissertation

• By Fall of your 2nd year, choose your Faculty Adviser and discuss with your faculty adviser who will be on your committee.
• Complete and submit the Nomination of Doctoral Committee Form at least one month before you take your orals.
• Contact Student Affairs to schedule a time and date to take your orals. Confirm the time and date with your committee.
• Your Adviser will let you know when you are ready to take your final orals and submit your dissertation online. When that time comes, arrange time, date and location with the student affairs office.
• If you still need more time and after you’ve advanced choose to do a Filing Fee instead please read this website carefully: https://grad.ucla.edu/academics/graduate-study/filing-fee-application/
• You must also complete the Filing Fee application found here: https://grad.ucla.edu/gasaa/etd/filingfee.pdf
• Important dates and workshops are found here: https://grad.ucla.edu/academics/calendar/thesis-dissertation-filing-deadlines-and-workshops/
• Should you choose the Filing Fee for a specific quarter, you must be registered and enrolled the quarter before AND you must submit a complete first draft of your dissertation to all committee members at the time you submit your filing fee application (in order to apply the filing fee, students must be registered and enrolled in at least 2 units the quarter before).

Faculty Research Interest See the faculty directory listing for current members and their interests at http://directory.stat.ucla.edu/ .

Doctor of Philosophy in Data Science

Developing future pioneers in data science

The School of Data Science at the University of Virginia is committed to educating the next generation of data science leaders. The Ph.D. in Data Science is designed to impart the skills and knowledge necessary to enable research and discovery in data science methods. Because the end goal is to extract knowledge and enable discovery from complex data, the program also boasts robust applied training that is geared toward interdisciplinary collaboration. Doctoral candidates will master the computational and mathematical foundations of data science, and develop competencies in data engineering, software development, data policy and ethics. 

Doctoral students in our program apprentice with faculty and pursue advanced research in an interdisciplinary, collaborative environment that is often focused on scientific discovery via data science methods. By serving as teaching assistants for the School’s undergraduate and graduate programs, they learn to be adroit educators and hone their critical thinking and communication skills.

LEARNING OUTCOMES

Pursuing a Ph.D. in Data Science will prepare you to become an expert in the field and work at the cutting edge of a new discipline. According to LinkedIn’s most recent Emerging Jobs Report, data science is booming and data scientist is one of the top three fastest growing jobs. A Ph.D. in Data Science from the University of Virginia opens career paths in academia, industry or government. Graduates of our program will:

• Understand data as a generic concept, and how data encodes and captures information
• Be fluent in modern data engineering techniques, and work with complex and large data sets
• Recognize ethical and legal issues relevant to data analytics and their impact on society 
• Develop innovative computational algorithms and novel statistical methods that transform data into knowledge
• Collaborate with research teams from a wide array of scientific fields 
• Effectively communicate methods and results to a variety of audiences and stakeholders
• Recognize the broad applicability of data science methods and models 

Graduates of the Ph.D. in Data Science will have contributed novel methodological research to the field of data science, demonstrated their work has impactful interdisciplinary applications and defended their methods in an open forum.

A Week in the Life: First-Year Ph.D. Student

Ph.D. Student Profile: Jade Preston

Ph.D. Student Profile: Beau LeBlond

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Statistics & Data Science

Dietrich college of humanities and social sciences, ph.d. programs, our ph.d. programs enable students to pursue a wide range of research opportunities, including constructing and implementing advanced methods of data analysis to address crucial cross-disciplinary questions, along with developing the fundamental theory that supports these methods..

Unique opportunities for our Ph.D. students include:

• We host four cross-disciplinary joint Ph.D. programs for students who want to specialize in machine learning , public policy , neuroscience , and the link between engineering and policy .
• Our faculty have deep involvement in a range of important, data-rich scientific collaborations, including in the areas of genetics, neuroscience, astronomy, and the social sciences. This allows students to have easy access to both the crucial questions in these fields, and to the data that can provide the answers.
• Students begin work on their Advanced Data Analysis Project in the second semester. This year-long, faculty/student collaboration, distinct from the thesis, provides an immediate intensive research experience.
• Carnegie Mellon is home to the first Machine Learning Department . Many of our faculty maintain joint appointments with this Department and they (and our students) have strong connections to this exciting and growing area of research.

The programs leading to the degree of   Doctor of Philosophy in Statistics   seek to strike a balance between theoretical and applied statistics. The Ph.D. program prepares students for university teaching and research careers, and for industrial and governmental positions involving research in new statistical methods. Four to five years are usually needed to complete all requirements for the Ph.D. degree.

These pages present the requirements for each of our Ph.D. programs.

The page   "Core Ph.D. Requirements"   lays out the requirements for all Ph.D. students, while each of the four joint programs are described under the Joint Ph.D. Degrees pages. Our Ph.D. students can also earn a   Master of Science in Statistics   as an intermediate step towards their ultimate goal.

Joint Ph.D. Programs

Statistics/machine learning, statistics/public policy, statistics/engineering and public policy, statistics/neural computation  .

Cornell University does not offer a separate Masters of Science (MS) degree program in the field of Statistics. Applicants interested in obtaining a masters-level degree in statistics should consider applying to Cornell's MPS Program in Applied Statistics.

Choosing a Field of Study

There are many graduate fields of study at Cornell University. The best choice of graduate field in which to pursue a degree depends on your major interests. Statistics is a subject that lies at the interface of theory, applications, and computing. Statisticians must therefore possess a broad spectrum of skills, including expertise in statistical theory, study design, data analysis, probability, computing, and mathematics. Statisticians must also be expert communicators, with the ability to formulate complex research questions in appropriate statistical terms, explain statistical concepts and methods to their collaborators, and assist them in properly communicating their results. If the study of statistics is your major interest then you should seriously consider applying to the Field of Statistics.

There are also several related fields that may fit even better with your interests and career goals. For example, if you are mainly interested in mathematics and computation as they relate to modeling genetics and other biological processes (e.g, protein structure and function, computational neuroscience, biomechanics, population genetics, high throughput genetic scanning), you might consider the Field of Computational Biology . You may wish to consider applying to the Field of Electrical and Computer Engineering if you are interested in the applications of probability and statistics to signal processing, data compression, information theory, and image processing. Those with a background in the social sciences might wish to consider the Field of Industrial and Labor Relations with a major or minor in the subject of Economic and Social Statistics. Strong interest and training in mathematics or probability might lead you to choose the Field of Mathematics . Lastly, if you have a strong mathematics background and an interest in general problem-solving techniques (e.g., optimization and simulation) or applied stochastic processes (e.g., mathematical finance, queuing theory, traffic theory, and inventory theory) you should consider the Field of Operations Research .

Residency Requirements

Students admitted to PhD program must be "in residence" for at least four semesters, although it is generally expected that a PhD will require between 8 and 10 semesters to complete. The chair of your Special Committee awards one residence unit after the satisfactory completion of each semester of full-time study. Fractional units may be awarded for unsatisfactory progress.

Your Advisor and Special Committee

The Director of Graduate Studies is in charge of general issues pertaining to graduate students in the field of Statistics. Upon arrival, a temporary Special Committee is also declared for you, consisting of the Director of Graduate Studies (chair) and two other faculty members in the field of Statistics. This temporary committee shall remain in place until you form your own Special Committee for the purposes of writing your doctoral dissertation. The chair of your Special Committee serves as your primary academic advisor; however, you should always feel free to contact and/or chat with any of the graduate faculty in the field of Statistics.

The formation of a Special Committee for your dissertation research should serve your objective of writing the best possible dissertation. The Graduate School requires that this committee contain at least three members that simultaneously represent a certain combination of subjects and concentrations. The chair of the committee is your principal dissertation advisor and always represents a specified concentration within the subject & field of Statistics. The Graduate School additionally requires PhD students to have at least two minor subjects represented on your special committee. For students in the field of Statistics, these remaining two members must either represent (i) a second concentration within the subject of Statistics, and one external minor subject; or, (ii) two external minor subjects. Each minor advisor must agree to serve on your special committee; as a result, the identification of these minor members should occur at least 6 months prior to your A examination.

Some examples of external minors include Computational Biology, Demography, Computer Science, Economics, Epidemiology, Mathematics, Applied Mathematics and Operations Research. The declaration of an external minor entails selecting (i) a field other than Statistics in which to minor; (ii) a subject & concentration within the specified field; and, (iii) a minor advisor representing this field/subject/concentration that will work with you in setting the minor requirements. Typically, external minors involve gaining knowledge in 3-5 graduate courses in the specified field/subject, though expectations can vary by field and even by the choice of advisor. While any choice of external minor subject is technically acceptable, the requirement that the minor representative serve on your Special Committee strongly suggests that the ideal choice(s) should share some natural connection with your choice of dissertation topic.

The fields, subjects and concentrations represented on your committee must be officially recognized by the Graduate School ; the Degrees, Subjects & Concentrations tab listed under each field of study provides this information. Information on the concentrations available for committee members chosen to represent the subject of Statistics can be found on the Graduate School webpage . 

Statistics PhD Travel Support

The Department of Statistics and Data Science has established a fund for professional travel for graduate students. The intent of the Department is to encourage travel that enhances the Statistics community at Cornell by providing funding for graduate students in statistics that will be presenting at conferences. Please review the Graduate Student Travel Award Policy website for more information. 

Completion of the PhD Degree

In addition to the specified residency requirements, students must meet all program requirements as outlined in Program Course Requirements and Timetables and Evaluations and Examinations, as well as complete a doctoral dissertation approved by your Special Committee. The target time to PhD completion is between 4 and 5 years; the actual time to completion varies by student.

Students should consult both the Guide to Graduate Study and Code of Legislation of the Graduate Faculty (available at www.gradschool.cornell.edu ) for further information on all academic and procedural matters pertinent to pursuing a graduate degree at Cornell University.

Computational and Data Science

Interdisciplinary Ph.D. in Computational & Data Science. Research-intensive, programming, communication skills.

Home » Program » Computational and Data Science, Ph.D.

Computational and Data Science, Ph.D.

The Computational and Data Science Ph.D. is an interdisciplinary program that includes faculty from Agriculture, Biology, Chemistry, Computer Science, Engineering Technology, Geosciences, Mathematical Sciences, and Physics and Astronomy.

The program is research-intensive and applied in nature, seeking to produce graduates with competency in the following three key areas:

• Mastery of the mathematical methods of computation as applied to scientific research investigations coupled with a firm understanding of the underlying fundamental science in at least one disciplinary specialization.
• Deep knowledge of programming languages, scientific programming, and computing technology so that graduates can adapt and grow as computing systems evolve
• Effective written and oral communication skills so that graduates may assume leadership positions in academia, national labs, and industry.

The Computational and Data Science Ph.D. program is for students who are working toward their doctoral degrees. However, with a few extra courses and requirements, most students in the program can complete a Master's degree in mathematics, Computer Science, or Data Science before they graduate.

Requirements

Information.

News Briefs

Alum find success in field after graduation

Dr. Robert Michael began his graduate studies at MTSU in 2008 in the Department of Mathematics. In 2014, he completed his Ph.D. in Computational Science and his master’s degree in Computer Science. His dissertation focused on computational chemistry.

After graduating, he became an HPC specialist at St. Jude Children’s Research Hospital, after which he became Oak Ridge National Laboratory’s Chief Data Architect. He is currently an HPC System Architect at Roche Sequencing Solutions.

Michael, J. R. (2014).  Analysis of Thermal Motion Effects on the Electron Density via Computational Simulations  (Order No. 3668039). Available from Dissertations & Theses @ Middle Tennessee State University; ProQuest Dissertations & Theses Global. (1647473197).

Alum's research focuses on mathematical models of tumor growth

Dr. Richard Ewool began his undergraduate studies in Ghana. After joining the Ph.D. program, his research and undergraduate studies focused on Mathematical models of tumor growth. After graduating, he became an Assistant Professor of Mathematics at Baptist Health Services University in Memphis.

Ewool, R. C. (2016).  Mathematical modeling and simulation of a multiscale tumor induced angiogenesis model  (Order No. 10146829). Available from Dissertations & Theses @ Middle Tennessee State University; ProQuest Dissertations & Theses Global. (1829637016).

Related Media

Since computational and data science involves using computers to solve scientific problems, graduates can work as research scientists in almost any field of science or engineering in industry or government, or at a university. MTSU’s program has focus areas in bioinformatics, biological modeling, computational chemistry, computational graph theory, computational physics, engineering and differential equations, high performance computing, and machine learning and remote sensing. In each of these areas, MTSU faculty and students are working on cutting-edge research projects that cut across traditional departmental boundaries.

Employers of MTSU alumni include

Our first graduates from the Computational and Data Science Ph.D. program have found jobs or received offers in companies and academic positions at universities including:

• St. Jude's Children's Hospital
• John Hopkin's University
• Southern Arkansas State University
• Texas A&M
• Oak Ridge National Laboratory
• Duke University

REQUIREMENTS

Dr. John Wallin

Dr. Rafet Al-Tobasei

Dr. Vishwas N Bedekar

Dr. Song Cui

Dr. Wandi Ding

Dr. Misa Faezipour

Dr. Don Hong

Dr. Steve Howard

Dr. Abdul Khaliq

Dr. Jing Kong

Dr. Rachel N. Leander

Dr. Yeqian Liu

Dr. Preston J. MacDougall

Dr. Vajira Manathunga

Dr. Lei Miao

Dr. Mina Mohebbi

Dr. Henrique Momm

Dr. Joshua L. Phillips

Dr. Khem Poudel

Dr. Jaishree Ranganathan

Dr. Ramchandra Rimal

Dr. William Robertson

Dr. Emmanuel Rowe

Dr. Arpan Man Sainju

Dr. Suk Jai Seo

Dr. David Chris Stephens

Dr. Hanna Terletska

Dr. Anatoliy Volkov

Dr. Donald M. Walker

Dr. Donglin (Forrest) Wang

Dr. Qiang Wu

Dr. Lu Xiong

Dr. Xin Yang

Dr. Dong Ye

Dr. Xiaoya Zha

Dr. Hongbo Zhang

Dr. Zhou (Joe) Zhang

INFORMATION

Assistantships.

Research and teaching assistantships, with stipends beginning at $20,100, are available on a competitive basis to full-time students in the COMS program. In addition to the stipend, the university also pays all tuition and most fees for assistantship holders. Non-Tennessee residents who are awarded a graduate assistantship are not required to pay out-of-state fees. To learn more about the types of graduate assistantships and to download an application, visit the Graduate Studies Assistantship page.

The College of Graduate Studies also awards a limited number of scholarships. For additional information and applications, visit the Graduate Studies Finance page.

In addition to assistantships and scholarships, MTSU's Office of Financial Aid assists graduate students seeking other forms of financial support while in school.

Student Forms

• COMS Student Handbook
• COMS Degree Plan (.docx)
• COMS Degree Plan (.pdf)
• COMS Qualifying Exam Proposal
• COMS Qualifying Exam Results
• MTSU Graduate Student Forms
• Travel and Reimbursement Forms
• 25Live for Scheduling Rooms

Research in Computational and Data Science

Computation is now regarded as an equal and indispensable partner, along with theory and experiment, in the advance of scientific knowledge. Numerical simulation enables the study of complex systems and natural phenomena that would be too expensive or dangerous, or even impossible, to study by direct experimentation. The quest for increasing levels of detail and realism in such simulations requires enormous computational capacity, and has provided the impetus for dramatic breakthroughs in computer algorithms and architectures. Due to these advances, computational scientists can now solve large-scale problems that were once thought intractable.

Computational Science is in a rapidly growing multidisciplinary area with connections to the sciences, mathematics, and computer science. The program focuses on the development of problem-solving methodologies and robust tools for the solution of scientific problems.

The Computational and Data Science (COMS) program is a broad multidisciplinary area that encompasses applications in science, applied mathematics, numerical analysis, and computer science. Computer models and computer simulations have become an important part of the research repertoire, supplementing (and in some cases replacing) experimentation. Going from application area to computational results requires domain expertise, mathematical modeling, numerical analysis, algorithm development, software implementation, program execution, analysis, validation, and visualization of results. The COMS program comprises all of the above.

MTSU’s program and research includes elements from computer science, applied mathematics, and science. The COMS program focuses on the integration of knowledge and methodologies from all of these disciplines, but is also distinct from the rest.

It is hard to capture how broad the program is without looking at some of the publications recently submitted. They are from across virtually every discipline. However, the common theme is the use of computers to solve cutting-edge scientific problems.

Publications

Publications of the Computational and Data Science Faculty 2018-Present

Bold indicates a faculty author. 

Al-Tobasei, Rafet,  Ali Ali, Andre L.S. Garcia, Daniela Lourenco, Tim Leeds, and Mohamed Salem. 2021. “Genomic Predictions for Fillet Yield and Firmness in Rainbow Trout Using Reduced-Density SNP Panels.”  BMC Genomics  22 (1).  https://doi.org/10.1186/s12864-021-07404-9 .

Ali, Ali,  Rafet Al-Tobasei , Brett Kenney, Timothy D. Leeds, and Mohamed Salem. 2018. “Integrated Analysis of LncRNA and MRNA Expression in Rainbow Trout Families Showing Variation in Muscle Growth and Fillet Quality Traits.”  Scientific Reports  8 (1).  https://doi.org/10.1038/s41598-018-30655-8 .

Ali, Ali,  Rafet Al-Tobasei , Daniela Lourenco, Tim Leeds, Brett Kenney, and Mohamed Salem. 2019. “Genome-Wide Association Study Identifies Genomic Loci Affecting Filet Firmness and Protein Content in Rainbow Trout.”  Frontiers in Genetics  10 (May).  https://doi.org/10.3389/fgene.2019.00386 .

———. 2020a. “Genome-Wide Scan for Common Variants Associated with Intramuscular Fat and Moisture Content in Rainbow Trout.”  BMC Genomics  21 (1).  https://doi.org/10.1186/s12864-020-06932-0 .

———. 2020b. “Genome-Wide Identification of Loci Associated with Growth in Rainbow Trout.”  BMC Genomics  21 (1).  https://doi.org/10.1186/s12864-020-6617-x.

Alrammah, Huda, and  Yi Gu . 2021. “Workflow Scheduling in Clouds Using Pareto Dominance for Makespan, Cost and Energy.” In  ICC 2021 - IEEE International Conference on Communications . IEEE.  https://doi.org/10.1109/ICC42927.2021.9500489 .

Alrammah, Huda,  Yi Gu , and Zhifeng Liu. 2020. “Tri-Objective Workflow Scheduling and Optimization in Heterogeneous Cloud Environments.” In  2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) . IEEE.  https://doi.org/10.1109/IPDPSW50202.2020.00129 .

Alshehri, Asma, John Ford, and  Rachel Leander . 2020. “The Impact of Maturation Time Distributions on the Structure and Growth of Cellular Populations.”  Mathematical Biosciences and Engineering  17 (2).  https://doi.org/10.3934/mbe.2020098 .

Alzahrani, S.S., and A .Q.M. Khaliq . 2019. “Fourier Spectral Exponential Time Differencing Methods for Multi-Dimensional Space-Fractional Reaction–Diffusion Equations.”  Journal of Computational and Applied Mathematics  361 (December).  https://doi.org/10.1016/j.cam.2019.04.001 .

Bagheri, Minoo, Hemant K. Tiwari, Anarina L. Murillo,  Rafet Al-Tobasei , Donna K. Arnett, Tobias Kind, Dinesh Kumar Barupal, et al. 2020. “A Lipidome-Wide Association Study of the Lipoprotein Insulin Resistance Index.”  Lipids in Health and Disease  19 (1).  https://doi.org/10.1186/s12944-020-01321-8 .

Barlow, Angela T., Natasha E. Gerstenschlager, Jeremy F. Strayer, Alyson E. Lischka,  D.   Christopher Stephens , Kristin S. Hartland, and J. Christopher Willingham. 2018. “Scaffolding for Access to Productive Struggle.”  Mathematics Teaching in the Middle School  23 (4).  https://doi.org/10.5951/mathteacmiddscho.23.4.0202 .

Barlow, Angela T., Alyson E. Lischka, James C. Willingham, Kristin Hartland, and  D. Christopher Stephens . 2018. “The Relationship of Implicit Theories to Elementary Teachers’ Patterns of Engagement in a Mathematics-Focused Professional Development Setting. .”  Mid-Western Educational Researcher  30 (3): 93–122.

Barron, Mace G.,  Ryan R. Otter , Kristin A. Connors, Aude Kienzler, and Michelle R. Embry. 2021. “Ecological Thresholds of Toxicological Concern: A Review.”  Frontiers in Toxicology  3 (March).  https://doi.org/10.3389/ftox.2021.640183 .

Beaubien, Gale B., Connor I. Olson, and  Ryan R. Otter . 2019. “The Role of Sexual Dimorphism and Tissue Selection in Ecotoxicological Studies Using the Riparian Spider Tetragnatha Elongata.”  Bulletin of Environmental Contamination and Toxicology  103 (2).  https://doi.org/10.1007/s00128-019-02632-y .

Beaubien, Gale B., Connor I. Olson, Andrew C. Todd, and  Ryan R. Otter . 2020. “The Spider Exposure Pathway and the Potential Risk to Arachnivorous Birds.”  Environmental Toxicology and Chemistry  39 (11).  https://doi.org/10.1002/etc.4848 .

Biala, T.A., and  A.Q.M. Khaliq . 2021. “A Fractional-Order Compartmental Model for the Spread of the COVID-19 Pandemic.”  Communications in Nonlinear Science and Numerical Simulation  98 (July).  https://doi.org/10.1016/j.cnsns.2021.105764 .

Bratsos, A.G., and  A.Q.M. Khaliq . 2019. “An Exponential Time Differencing Method of Lines for Burgers–Fisher and Coupled Burgers Equations.”  Journal of Computational and Applied Mathematics  356 (August).  https://doi.org/10.1016/j.cam.2019.01.028 .

Brown, Ei, Zackery D. Fleischman, Larry D. Merkle,  Emmanuel Rowe , Arnold Burger, Stephen A. Payne, and Mark Dubinskii. 2018. “Optical Spectroscopy of Holmium Doped K2LaCl5.”  Journal of Luminescence  196 (April).  https://doi.org/10.1016/j.jlumin.2017.12.040 .

Brown, Ei, Zackery D. Fleischman, Larry D. Merkle,  Emmanuel Rowe , Arnold Burger, Stephen Payne, and Mark Dubinskiy. 2018. “Infrared Absorption and Fluorescence Properties of Holmium Doped Potassium Lanthanum Chloride (Conference Presentation).” In  Laser Technology for Defense and Security XIV , edited by Mark Dubinskiy and Timothy C. Newell. SPIE.  https://doi.org/10.1117/12.2309578 .

Castillo, Carlos,  Henrique G. Momm , Robert R. Wells, Ronald L. Bingner, and Rafael Pérez. 2021. “A GIS Focal Approach for Characterizing Gully Geometry.”  Earth Surface Processes and Landforms  46 (9).  https://doi.org/10.1002/esp.5122 .

Chapagain, Pratima, Brock Arivett, Beth M. Cleveland,  Donald M. Walker , and Mohamed Salem. 2019. “Analysis of the Fecal Microbiota of Fast- and Slow-Growing Rainbow Trout (Oncorhynchus Mykiss).”  BMC Genomics  20 (1).  https://doi.org/10.1186/s12864-019-6175-2 .

Chapagain, Pratima,  Donald Walker , Tim Leeds, Beth M. Cleveland, and Mohamed Salem. 2020. “Distinct Microbial Assemblages Associated with Genetic Selection for High- and Low- Muscle Yield in Rainbow Trout.”  BMC Genomics  21 (1).  https://doi.org/10.1186/s12864-020-07204-7 .

Chen, Shanshan, Junping Shi, Zhisheng Shuai, and  Yixiang Wu . 2019. “Spectral Monotonicity of Perturbed Quasi-Positive Matrices with Applications in Population Dynamics,” November.

———. 2020. “Asymptotic Profiles of the Steady States for an SIS Epidemic Patch Model with Asymmetric Connectivity Matrix.”  Journal of Mathematical Biology  80 (7).  https://doi.org/10.1007/s00285-020-01497-8 .

Connors, Kristin A., Amy Beasley, Mace G. Barron, Scott E. Belanger, Mark Bonnell, Jessica L. Brill, Dick de Zwart, et al. 2019. “Creation of a Curated Aquatic Toxicology Database: EnviroTox.”  Environmental Toxicology and Chemistry  38 (5).  https://doi.org/10.1002/etc.4382 .

Cui, Song, Qiang Wu , James West, and Jiangping Bai. 2019. “Machine Learning-Based Microarray Analyses Indicate Low-Expression Genes Might Collectively Influence PAH Disease.”  PLOS Computational Biology  15 (8).  https://doi.org/10.1371/journal.pcbi.1007264 .

Cui, Xia, Thomas Goff,  Song Cui , Dorothy Menefee,  Qiang Wu , Nithya Rajan, Shyam Nair, Nate Phillips, and Forbes Walker. 2021. “Predicting Carbon and Water Vapor Fluxes Using Machine Learning and Novel Feature Ranking Algorithms.”  Science of The Total Environment  775 (June).  https://doi.org/10.1016/j.scitotenv.2021.145130 .

Deng, Keng, Glenn F. Webb, and  Yixiang Wu . 2020. “Analysis of Age and Spatially Dependent Population Model: Application to Forest Growth.”  Nonlinear Analysis: Real World Applications  56 (December).  https://doi.org/10.1016/j.nonrwa.2020.103164 .

Ding, Wandi , Ryan Florida, Jeffery Summers, Puran Nepal, and Ben Burton. 2019. “Experience and Lessons Learned from Using SIMIODE Modeling Scenarios.”  PRIMUS  29 (6).  https://doi.org/10.1080/10511970.2018.1488318 .

• Barbosa, Salvador . 2020. “Using Holographically Compressed Embeddings in Question Answering.” In  Computer Science & Information Technology . AIRCC Publishing Corporation.  https://doi.org/10.5121/csit.2020.100919 .

Ellingham, M. N., Songling Shan,  Dong Ye , and  Xiaoya Zha.  2021. “Toughness and Spanning Trees in K4‐minor‐free Graphs.”  Journal of Graph Theory  96 (3).  https://doi.org/10.1002/jgt.22620 .

Epifanovsky, Evgeny, Andrew T. B. Gilbert, Xintian Feng, Joonho Lee, Yuezhi Mao, Narbe Mardirossian, Pavel Pokhilko,  et al . 2021. “Software for the Frontiers of Quantum Chemistry: An Overview of Developments in the Q-Chem 5 Package.”  The Journal of Chemical Physics  155 (8).  https://doi.org/10.1063/5.0055522 .

Faezipour, Misagh,  and Miad Faezipour. 2020. “Sustainable Smartphone-Based Healthcare Systems: A Systems Engineering Approach to Assess the Efficacy of Respiratory Monitoring Apps.”  Sustainability  12 (12).  https://doi.org/10.3390/su12125061 .

———. 2021a. “Smart Healthcare Monitoring Apps with a Flavor of Systems Engineering.” In .  https://doi.org/10.1007/978-3-030-71051-4_48 .

———. 2021b. “Efficacy of Smart EEG Monitoring Amidst the COVID-19 Pandemic.”  Electronics  10 (9).  https://doi.org/10.3390/electronics10091001 .

———. 2021c. “System Dynamics Modeling for Smartphone-Based Healthcare Tools: Case Study on ECG Monitoring.”  IEEE Systems Journal  15 (2).  https://doi.org/10.1109/JSYST.2020.3009187 .

Faezipour, Misagh , and Susan Ferreira. 2018. “A System Dynamics Approach for Sustainable Water Management in Hospitals.”  IEEE Systems Journal  12 (2).  https://doi.org/10.1109/JSYST.2016.2573141 .

Feng, Yunlong, and  Qiang Wu.  2020. “Learning under (1 + ϵ)-Moment Conditions.”  Applied and Computational Harmonic Analysis  49 (2).  https://doi.org/10.1016/j.acha.2020.05.009 .

———. 2021. “A Framework of Learning Through Empirical Gain Maximization.”  Neural Computation  33 (6).  https://doi.org/10.1162/neco_a_01384 .

Fernando, Kushantha, and  Vajira Manathunga . 2021. “An Alternative Approach to Evaluate American Options Price Using HJM Approach,” September.

Fitzgibbon, W. E., J. J. Morgan, G. F. Webb, And  Y. Wu.  2020. “Analysis Of A Reaction–Diffusion Epidemic Model With Asymptomatic Transmission.”  Journal of Biological Systems  28 (03).  https://doi.org/10.1142/S0218339020500126 .

Fitzgibbon, William E., Jeffery J. Morgan, Glenn F. Webb, and  Yixiang Wu.  2019. “Spatial Models of Vector-Host Epidemics with Directed Movement of Vectors over Long Distances.”  Mathematical Biosciences  312 (June).  https://doi.org/10.1016/j.mbs.2019.04.003 .

Fitzgibbon, William E., Jeffrey J. Morgan, Glenn F. Webb, and  Yixiang Wu . 2020. “Modelling the Aqueous Transport of an Infectious Pathogen in Regional Communities: Application to the Cholera Outbreak in Haiti.”  Journal of The Royal Society Interface  17 (169).  https://doi.org/10.1098/rsif.2020.0429 .

Fitzgibbon, William, Jeffrey Morgan, Glenn Webb, and  Yixiang Wu . 2020. “Maritime Transport and the Threat of Bio Invasion and the Spread of Infectious Disease.” In  Computational Methods in Applied Sciences .  https://doi.org/10.1007/978-3-030-37752-6_5 .

Gerstenschlager, Natasha, Angela T. Barlow, Alyson Lischka, Lucy Watson, Jeremy Strayer, D.  Christopher Stephens , Kristin S. Hartland, and James C. Willingham. 2021. “Double Demonstration Lessons: Authentically Participating in an Inquiry Stance.”  Mathematics Teacher Educator  9 (2).  https://doi.org/10.5951/MTE.2020.0048 .

Grajal-Puche, Alejandro, Christopher M. Murray, Matthew Kearley, Mark Merchant, Christopher Nix, Jonathan K. Warner, and  Donald M. Walker . 2020. “Microbial Assemblage Dynamics Within the American Alligator Nesting Ecosystem: A Comparative Approach Across Ecological Scales.”  Microbial Ecology  80 (3).  https://doi.org/10.1007/s00248-020-01522-9 .

Grisnik, Matthew, Olivia Bowers, Andrew J Moore, Benjamin F Jones, Joshua R Campbell, and  Donald M Walker . 2020. “The Cutaneous Microbiota of Bats Has in Vitro Antifungal Activity against the White Nose Pathogen.”  FEMS Microbiology Ecology  96 (2).  https://doi.org/10.1093/femsec/fiz193 .

Grisnik, Matthew, Jacob Leys, Danny Bryan, Rebecca Hardman, Debra Miller, Vincent Cobb, Chris Ogle,  et al.  2018. “Host and Geographic Range of Snake Fungal Disease in Tennessee, USA.”  Herpetological Review  49 (October): 682–90.

Gu, Yi,  and Chandu Budati. 2020. “Energy-Aware Workflow Scheduling and Optimization in Clouds Using Bat Algorithm.”  Future Generation Computer Systems  113 (December).  https://doi.org/10.1016/j.future.2020.06.031 .

Gulizia, J. P., K. M. Downs, and  S. Cui . 2019. “Kudzu (  Pueraria Montana  Var.  Lobata  ) Age Variability Effects on Total and Nutrient-Specific  in Situ  Rumen Degradation.”  Journal of Applied Animal Research  47 (1).  https://doi.org/10.1080/09712119.2019.1652615 .

Gulizia, Joseph, Kevin Downs, and  Song Cui.  2019. “55 The Influence of Kudzu (Pueraria Montana Var. Lobata) Age on in Situ Rumen Degradation.”  Journal of Animal Science  97 (Supplement_1).  https://doi.org/10.1093/jas/skz053.194 .

Guo, X., T Hu, and  Q. Wu.  2020. “Distributed Minimum Error Entropy Algorithms.”  Journal of Machine Learning Research  21 (126): 1–31.

Guo, Xin, Lexin Li, and  Qiang Wu . 2020. “Modeling Interactive Components by Coordinate Kernel Polynomial Models.”  Mathematical Foundations of Computing  3 (4).  https://doi.org/10.3934/mfc.2020010 .

Győri, Ervin, Michael D. Plummer,  Dong Ye,  and  Xiaoya Zha.  2020. “Cycle Traversability for Claw-Free Graphs and Polyhedral Maps.”  Combinatorica  40 (3).  https://doi.org/10.1007/s00493-019-4042-z .

Haruna, Samuel I., Stephen H. Anderson, Ranjith P. Udawatta, Clark J. Gantzer, Nathan C. Phillips, S ong Cui , and Ying Gao. 2020. “Improving Soil Physical Properties through the Use of Cover Crops: A Review.”  Agrosystems, Geosciences & Environment  3 (1).  https://doi.org/10.1002/agg2.20105 .

He, Fangchao, and  Qiang Wu . 2019. “Bias Corrected Regularization Kernel Method in Ranking.”  Analysis and Applications  17 (01).  https://doi.org/10.1142/S0219530518500161 .

He, Jinghua, Erling Wei,  Dong Ye , and Shaohui Zhai. 2019. “On Perfect Matchings in Matching Covered Graphs.”  Journal of Graph Theory  90 (4).  https://doi.org/10.1002/jgt.22411 .

Hill, Aubree J., Jacob E. Leys, Danny Bryan, Fantasia M. Erdman, Katherine S. Malone, Gabrielle N. Russell, Roger D. Applegate,  et al . 2018. “Common Cutaneous Bacteria Isolated from Snakes Inhibit Growth of Ophidiomyces Ophiodiicola.”  EcoHealth  15 (1).  https://doi.org/10.1007/s10393-017-1289-y .

Hong, Don . 2019. “On Scattered Data Representations Using Bivariate Splines. Handbook of Analytic-Computational Methods in Applied Mathematics.” In  Handbook of Analytic-Computational Methods in Applied Mathematics , edited by George Anastassiou. Chapman and Hall/CRC.  https://doi.org/10.1201/9780429123610 .

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Schulman, Alan, and  Salvador Barbosa . 2018. “Text Genre Classification Using Only Parts of Speech.” In  2018 International Conference on Computational Science and Computational Intelligence (CSCI) . IEEE.  https://doi.org/10.1109/CSCI46756.2018.00236 .

Seo, Suk J . 2021. “Fault-Tolerant Detectors for Distinguishing Sets in Cubic Graphs.”  Discrete Applied Mathematics  293 (April).  https://doi.org/10.1016/j.dam.2021.01.008 .

Sharma, Sumit, Nithya Rajan,  Song Cui , Stephen Maas, Kenneth Casey, Srinivasulu Ale, and Russel Jessup. 2019. “Carbon and Evapotranspiration Dynamics of a Non-Native Perennial Grass with Biofuel Potential in the Southern U.S. Great Plains.”  Agricultural and Forest Meteorology  269–270 (May).  https://doi.org/10.1016/j.agrformet.2019.01.037 .

Shi, Junping,  Yixiang Wu , and Xingfu Zou. 2020. “Coexistence of Competing Species for Intermediate Dispersal Rates in a Reaction–Diffusion Chemostat Model.”  Journal of Dynamics and Differential Equations  32 (2).  https://doi.org/10.1007/s10884-019-09763-0 .

Shuttleworth, Lucas A., David I. Guest, and  Donald M. Walker . 2018. “The Fungus, the Code and the Mysterious Publication Date: Why Gnomoniopsis Smithogilvyi Is Still the Correct Name for the Chestnut Rot Fungus.”  IMA Fungus  9 (2).  https://doi.org/10.1007/BF03449443 .

Smith-Peavier, Emily, Grant E. Gardner, and  Ryan Otter . 2019. “PowerPoint Use in the Undergraduate Biology Classroom: Perceptions and Impacts on Student Learning.”  Journal of College Science Teaching  48 (n3): 74–83.

Snyder, Shawn D., William B. Sutton, and  Donald M. Walke r. 2020. “Prevalence of Ophidiomyces Ophiodiicola, the Causative Agent of Snake Fungal Disease, in the Interior Plateau Ecoregion of Tennessee, USA.”  Journal of Wildlife Diseases  56 (4).  https://doi.org/10.7589/2019-04-109 .

Sun, Hongwei, and  Qiang Wu . 2020. “Optimal Rates of Distributed Regression with Imperfect Kernels.”  Journal of Machine Learning Research  22 (171): 1–34.

Sun, Yuyang, Peizhou Yan, Zhengzheng Li, Jiancheng Zou, and  Don Hong . 2020. “Driver Fatigue Detection System Based on Colored and Infrared Eye Features Fusion.”  Computers, Materials & Continua  63 (3).  https://doi.org/10.32604/cmc.2020.09763 .

Swindall, Matthew I., Gregory Croisdaile, Chase C. Hunter, Ben Keener, Alex C. Williams, James H. Brusuelas, Nita Krevens, Melissa Sellew, Lucy Fortson, and  John F. Wallin . 2021. “Exploring Learning Approaches for Ancient Greek Character Recognition with Citizen Science Data.” In  Exploring Learning Approaches for Ancient Greek Character Recognition with Citizen Science Data . IEEE 17th International Conference on eScience.

Syzonenko, Ivan, and  Joshua L. Phillips . 2018. “Hybrid Spectral/Subspace Clustering of Molecular Dynamics Simulations.” In  Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics . New York, NY, USA: ACM.  https://doi.org/10.1145/3233547.3233595 .

———. 2020. “Accelerated Protein Folding Using Greedy-Proximal A*.”  Journal of Chemical Information and Modeling  60 (6).  https://doi.org/10.1021/acs.jcim.9b01194 .

Taguas, E.V., R.L. Bingner,  H.G. Momm , R. Wells, and M.A. Locke. 2021. “Modelling Scenarios of Soil Properties and Managements in Olive Groves at the Micro-Catchment Scale with the AnnAGNPS Model to Quantify Organic Carbon.”  CATENA  203 (August).  https://doi.org/10.1016/j.catena.2021.105333 .

Tam, K.-M., Y. Zhang,  H. Terletska , Y. Wang, M. Eisenbach, L. Chioncel, and J. Moreno. 2021. “Application of the Locally Self-Consistent Embedding Approach to the Anderson Model with Non-Uniform Random Distributions.”  Annals of Physics , April.  https://doi.org/10.1016/j.aop.2021.168480 .

Tanguay, P., M. Blais, A. Potvin, D. Stewart,  D. Walker , N. Nadeau-Thibodeau, P. DesRochers, and D. Rioux. 2018. “QPCR Quantification of  Ophiognomonia Clavigignenti-Juglandacearum  from Infected Butternut Trees under Different Release Treatments.”  Forest Pathology  48 (3).  https://doi.org/10.1111/efp.12418 .

Terletska, H. , A. Moilanen, K.-M. Tam, Y. Zhang, Y. Wang, M. Eisenbach, N.S. Vidhyadhiraja, L. Chioncel, and J. Moreno. 2021. “Non-Local Corrections to the Typical Medium Theory of Anderson Localization.”  Annals of Physics , March.  https://doi.org/10.1016/j.aop.2021.168454 .

Terletska, Hanna , Tianran Chen, Joseph Paki, and Emanuel Gull. 2018. “Charge Ordering and Nonlocal Correlations in the Doped Extended Hubbard Model.”  Physical Review B  97 (11).  https://doi.org/10.1103/PhysRevB.97.115117 .

Terletska, Hanna , Sergei Iskakov, Thomas Maier, and Emanuel Gull. 2021. “Dynamical Cluster Approximation Study of Electron Localization in the Extended Hubbard Model.”  Physical Review B  104 (8).  https://doi.org/10.1103/PhysRevB.104.085129 .

Terletska, Hanna , Yi Zhang, Ka-Ming Tam, Tom Berlijn, Liviu Chioncel, N. Vidhyadhiraja, and Mark Jarrell. 2018. “Systematic Quantum Cluster Typical Medium Method for the Study of Localization in Strongly Disordered Electronic Systems.”  Applied Sciences  8 (12).  https://doi.org/10.3390/app8122401 .

Tong, Hongzhi, and  Qiang Wu . 2020. “Moving Quantile Regression.”  Journal of Statistical Planning and Inference  205 (March).  https://doi.org/10.1016/j.jspi.2019.06.003 .

Tzacheva, A., and  R. Jaishree . 2018. “EMOTION MINING FROM STUDENT COMMENTS A LEXICON BASED APPROACH FOR PEDAGOGICAL INNOVATION ASSESSMENT.”  The European Journal of Education and Applied Psychology , September.  https://doi.org/10.29013/EJEAP-18-3-3-13 .

Tzacheva, Angelina A.,  Jaishree Ranganathan , and Arunkumar Bagavathi. 2020. “Action Rules for Sentiment Analysis Using Twitter.”  International Journal of Social Network Mining  3 (1).  https://doi.org/10.1504/IJSNM.2020.105728 .

Tzacheva, Angelina,  Jaishree Ranganathan , and Rajendra Jadi. 2019. “Multi-Label Emotion Mining From Student Comments.” In  Proceedings of the 2019 4th International Conference on Information and Education Innovations - ICIEI 2019 . New York, New York, USA: ACM Press.  https://doi.org/10.1145/3345094.3345112 .

Tzacheva, Angelina,  Jaishree Ranganathan , and Sai Yesawy Mylavarapu. 2020. “Actionable Pattern Discovery for Tweet Emotions.” In  Advances in Intelligent Systems and Computing , 46–57.  https://doi.org/10.1007/978-3-030-20454-9_5 .

Varadwaj, Pradeep R., Arpita Varadwaj, Helder M. Marques, and  Preston J. MacDougall . 2019. “The Chalcogen Bond: Can It Be Formed by Oxygen?”  Physical Chemistry Chemical Physics  21 (36).  https://doi.org/10.1039/C9CP03783G .

Walker, Donald M ., Aubree J. Hill, Molly A. Albecker, Michael W. McCoy, Matthew Grisnik, Alexander Romer, Alejandro Grajal-Puche, et al. 2020. “Variation in the Slimy Salamander (Plethodon Spp.) Skin and Gut-Microbial Assemblages Is Explained by Geographic Distance and Host Affinity.”  Microbial Ecology  79 (4).  https://doi.org/10.1007/s00248-019-01456-x .

Walker, Donald M , Jacob E Leys, Matthew Grisnik, Alejandro Grajal-Puche, Christopher M Murray, and Matthew C Allender. 2019. “Variability in Snake Skin Microbial Assemblages across Spatial Scales and Disease States.”  The ISME Journal  13 (9): 2209–22.  https://doi.org/10.1038/s41396-019-0416-x .

Walker, Donald M , Christopher M Murray, Doug Talbert, Paul Tinker, Sean P Graham, and Thomas W Crowther. 2018. “A Salamander’s Top down Effect on Fungal Communities in a Detritivore Ecosystem.”  FEMS Microbiology Ecology  94 (12).  https://doi.org/10.1093/femsec/fiy168 .

Wallerberger, Markus, Sergei Iskakov, Alexander Gaenko, Joseph Kleinhenz, Igor Krivenko, Ryan Levy, Jia Li,  et al.  2018. “Updated Core Libraries of the ALPS Project,” November.

Wang, Donglin, Honglan Xu, and  Qiang Wu . 2020. “Averaging versus Voting: A Comparative Study of Strategies for Distributed Classification.”  Mathematical Foundations of Computing  3 (3).  https://doi.org/10.3934/mfc.2020017 .

Wang, Matthew, Dwayne John, Jianguo Yu, Emil Proynov, Fenglai Liu, Benjamin G. Janesko, and  Jing Kong . 2019. “Performance of New Density Functionals of Nondynamic Correlation on Chemical Properties.”  The Journal of Chemical Physics  150 (20).  https://doi.org/10.1063/1.5082745 .

Wang, Yiting, and  Jing Kong . 2021. “Efficient Spherical Surface Integration of Gauss Functions in Three-Dimensional Spherical Coordinates and the Solution for the Modified Bessel Function of the First Kind.”  Journal of Mathematical Chemistry  59 (2).  https://doi.org/10.1007/s10910-020-01204-4 .

Wang, Yiting, Emil Proynov, and  Jing Kong . 2021. “Model DFT Exchange Holes and the Exact Exchange Hole: Similarities and Differences.”  The Journal of Chemical Physics  154 (2).  https://doi.org/10.1063/5.0031995 .

Weatherly, Jessie, Piero Macchi, and  Anatoliy Volkov . 2021. “On the Calculation of the Electrostatic Potential, Electric Field and Electric Field Gradient from the Aspherical Pseudoatom Model. II. Evaluation of the Properties in an Infinite Crystal.”  Acta Crystallographica Section A Foundations and Advances  77 (5).  https://doi.org/10.1107/S2053273321005532 .

Weh, A., Y. Zhang, A. Östlin,  H. Terletska , D. Bauernfeind, K.-M. Tam, H. G. Evertz, K. Byczuk, D. Vollhardt, and L. Chioncel. 2021. “Dynamical Mean-Field Theory of the Anderson-Hubbard Model with Local and Nonlocal Disorder in Tensor Formulation.”  Physical Review B  104 (4).  https://doi.org/10.1103/PhysRevB.104.045127 .

West, Graham, Matthew Ogden,  John Wallin , Zachariah Sinkala, and William Smith. 2020. “Optimizing Numerical Simulations of Colliding Galaxies. I. Fitness Functions and Optimization Algorithms.”  Research Notes of the AAS  4 (136).

Williams, Arthur, and  Joshua Phillips . 2020. “Transfer Reinforcement Learning Using Output-Gated Working Memory.”  Proceedings of the AAAI Conference on Artificial Intelligence  34 (02).  https://doi.org/10.1609/aaai.v34i02.5488 .

Willingham, James C., Angela T. Barlow,  D. Christopher Stephens , Alyson E. Lischka, and Kristin S. Hartland. 2021. “Mindset Regarding Mathematical Ability in K‐12 Teachers.”  School Science and Mathematics  121 (4).  https://doi.org/10.1111/ssm.12466 .

Wu, Yezhou, Yujun Yang, and  Dong Ye . 2018. “A Note on Median Eigenvalues of Bipartite Graphs.”  MATCH Communications in Mathematical and in Computer Chemistry  80: 853–62.

Wu, Yezhou, and  Dong Ye . 2018. “Circuit Covers of Cubic Signed Graphs.”  Journal of Graph Theory  89 (1).  https://doi.org/10.1002/jgt.22238 .

———. 2020. “Minimum $T$-Joins and Signed-Circuit Covering.”  SIAM Journal on Discrete Mathematics  34 (2).  https://doi.org/10.1137/18M1226105 .

Wu, Yixiang , and Xingfu Zou. 2018. “Dynamics and Profiles of a Diffusive Host–Pathogen System with Distinct Dispersal Rates.”  Journal of Differential Equations  264 (8).  https://doi.org/10.1016/j.jde.2017.12.027 .

Xiong, Lu , and  Don Hong . 2020. “Using Monte Carlo Simulation to Predict Captive Insurance Solvency.” In  Proceedings of the 2020 the 4th International Conference on Compute and Data Analysis . New York, NY, USA: ACM.  https://doi.org/10.1145/3388142.3388171 .

Xu, Shuzhe,  Salvador E. Barbosa , and  Don Hong . 2020. “BERT Feature Based Model for Predicting the Helpfulness Scores of Online Customers Reviews.” In .  https://doi.org/10.1007/978-3-030-39442-4_21 .

Xu, Yi, and  Yeqian Liu . 2021. “Bias Adjustment Methods for Analysis of a Non-Randomized Controlled Trials of Right Heart Catheterization for Patients in ICU.”  Biomedical Statistics and Informatics  6 (2).  https://doi.org/10.11648/j.bsi.20210602.12 .

Yang, Xuan, Zhou Li, S ong Cui , Quan Cao, Jianqiang Deng, Xingfa Lai, and Yuying Shen. 2020. “Cropping System Productivity and Evapotranspiration in the Semiarid Loess Plateau of China under Future Temperature and Precipitation Changes: An APSIM-Based Analysis of Rotational vs. Continuous Systems.”  Agricultural Water Management  229 (February).  https://doi.org/10.1016/j.agwat.2019.105959 .

Yang, Xuan, Lina Zheng, Qian Yang, Zikui Wang,  Song Cui , and Yuying Shen. 2018. “Modelling the Effects of Conservation Tillage on Crop Water Productivity, Soil Water Dynamics and Evapotranspiration of a Maize-Winter Wheat-Soybean Rotation System on the Loess Plateau of China Using APSIM.”  Agricultural Systems  166 (October).  https://doi.org/10.1016/j.agsy.2018.08.005 .

Yang, Yujun, and  Dong Ye . 2018. “Inverses of Bipartite Graphs.”  Combinatorica  38 (5).  https://doi.org/10.1007/s00493-016-3502-y .

Yasarer, Lindsey M. W., Ronald L. Bingner, and  Henrique G. Momm . 2018. “Characterizing Ponds in a Watershed Simulation and Evaluating Their Influence on Streamflow in a Mississippi Watershed.”  Hydrological Sciences Journal  63 (2).  https://doi.org/10.1080/02626667.2018.1425954 .

Ye, Dong . 2018. “Maximum Matchings in Regular Graphs.”  Discrete Mathematics  341 (5).  https://doi.org/10.1016/j.disc.2018.01.016 .

Yeqian, Liu . 2019. “A Signal Detection Analysis of World Health Organization’s Pharmacovigilance Database.”  International Journal of Clinical Biostatistics and Biometrics  5 (2).  https://doi.org/10.23937/2469-5831/1510023 .

Yeqian, Liu , and  Yingxiao Huan g. 2020. “Semiparametric Likelihood Estimation with Clayton-Oakes Model for Multivariate Current Status Data.”  Journal of Biostatistics & Biometrics .  https://doi.org/10.29011/JBSB-109.100009 .

Zhai, Shaohui, Dalal Alrowaili, and  Dong Ye.  2018. “Clar Structures vs Fries Structures in Hexagonal Systems.”  Applied Mathematics and Computation  329 (July).  https://doi.org/10.1016/j.amc.2018.02.014 .

Zhai, Shaohui, Erling Wei, Jinghua He, and  Dong Ye . 2019. “Homeomorphically Irreducible Spanning Trees in Hexangulations of Surfaces.”  Discrete Mathematics  342 (10).  https://doi.org/10.1016/j.disc.2019.01.032 .

Zhang, Li, You Lu, Rong Luo,  Dong Ye , and Shenggui Zhang. 2020. “Edge Coloring of Signed Graphs.”  Discrete Applied Mathematics  282 (August).  https://doi.org/10.1016/j.dam.2019.12.004 .

Zhang, Ning, and  Qiang Wu . 2019. “Online Learning for Supervised Dimension Reduction.”  Mathematical Foundations of Computing  2 (2): 95–106.  https://doi.org/10.3934/mfc.2019008 .

Zhang, Ning, Zhou Yu, and  Qiang Wu . 2018. “Overlapping Sliced Inverse Regression for Dimension Reduction.”  Analysis and Applications  17 (5): 715–36.

Zhang, Yi, R. Nelson, K.-M. Tam, W. Ku, U. Yu, N. S. Vidhyadhiraja,  H. Terletska , J. Moreno, M. Jarrell, and T. Berlijn. 2018. “Origin of Localization in Ti-Doped Si.”  Physical Review B  98 (17).  https://doi.org/10.1103/PhysRevB.98.174204 .

Zhang, Yi,  Hanna Terletska , Ka-Ming Tam, Yang Wang, Markus Eisenbach, Liviu Chioncel, and Mark Jarrell. 2019. “Locally Self-Consistent Embedding Approach for Disordered Electronic Systems.”  Physical Review B  100 (5).  https://doi.org/10.1103/PhysRevB.100.054205 .

Zheng, Shao-Liang, Yu-Sheng Chen, Xiaoping Wang, Christina Hoffmann, and  Anatoliy Volkov . 2018. “From the Source: Student-Centred Guest Lecturing in a Chemical Crystallography Class.”  Journal of Applied Crystallography  51 (3).  https://doi.org/10.1107/S1600576718004120 .

Zheng, Xiaoqing, Hongwei Sun, and  Qiang Wu . 2021. “Regularized Least Square Kernel Regression for Streaming Data.”  Communications in Mathematical Sciences  19 (6).  https://doi.org/10.4310/CMS.2021.v19.n6.a4 .

Zou, Jiancheng, Zhengzheng Li, Zhijun Guo, and  Don Hong . 2019. “Super-Resolution Reconstruction of Images Based on Microarray Camera.”  Computers, Materials & Continua  60 (1).  https://doi.org/10.32604/cmc.2019.05795 .

Zou, Jiancheng, Na Zhu, Bailin Ge, and  Don Hong . 2021. “Elderly Fall Detection Based on Improved SSD Algorithm.”  Journal of New Media  3 (1).  https://doi.org/10.32604/jnm.2021.017763 .

Research Groups

The Faculty in the Computational Science Program at MTSU have a diverse set of research interests that cross between traditional departmental boundaries. The groups below outline some of the core research interests of our faculty. In some cases, faculty straddle two or more of the areas below. However, for simplicity, faculty are only associated with a single group on this page.

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PhD in Physics, Statistics, and Data Science

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Many PhD students in the MIT Physics Department incorporate probability, statistics, computation, and data analysis into their research. These techniques are becoming increasingly important for both experimental and theoretical Physics research, with ever-growing datasets, more sophisticated physics simulations, and the development of cutting-edge machine learning tools. The Interdisciplinary Doctoral Program in Statistics (IDPS)  is designed to provide students with the highest level of competency in 21st century statistics, enabling doctoral students across MIT to better integrate computation and data analysis into their PhD thesis research.

Admission to this program is restricted to students currently enrolled in the Physics doctoral program or another participating MIT doctoral program. In addition to satisfying all of the requirements of the Physics PhD, students take one subject each in probability, statistics, computation and statistics, and data analysis, as well as the Doctoral Seminar in Statistics, and they write a dissertation in Physics utilizing statistical methods. Graduates of the program will receive their doctoral degree in the field of “Physics, Statistics, and Data Science.”

Doctoral students in Physics may submit an Interdisciplinary PhD in Statistics Form between the end of their second semester and penultimate semester in their Physics program. The application must include an endorsement from the student’s advisor, an up-to-date CV, current transcript, and a 1-2 page statement of interest in Statistics and Data Science.

The statement of interest can be based on the student’s thesis proposal for the Physics Department, but it must demonstrate that statistical methods will be used in a substantial way in the proposed research. In their statement, applicants are encouraged to explain how specific statistical techniques would be applied in their research. Applicants should further highlight ways that their proposed research might advance the use of statistics and data science, both in their physics subfield and potentially in other disciplines. If the work is part of a larger collaborative effort, the applicant should focus on their personal contributions.

For access to the selection form or for further information, please contact the IDSS Academic Office at  [email protected] .

Required Courses

Courses in this list that satisfy the Physics PhD degree requirements can count for both programs. Other similar or more advanced courses can count towards the “Computation & Statistics” and “Data Analysis” requirements, with permission from the program co-chairs. The IDS.190 requirement may be satisfied instead by IDS.955 Practical Experience in Data, Systems, and Society, if that experience exposes the student to a diverse set of topics in statistics and data science. Making this substitution requires permission from the program co-chairs prior to doing the practical experience.

• IDS.190 – Doctoral Seminar in Statistics and Data Science ( may be substituted by IDS.955 Practical Experience in Data, Systems and Society )
• 6.7700[J] Fundamentals of Probability or
• 18.675 – Theory of Probability
• 18.655 – Mathematical Statistics or
• 18.6501 – Fundamentals of Statistics or
• IDS.160[J] – Mathematical Statistics: A Non-Asymptotic Approach
• 6.C01/6.C51 – Modeling with Machine Learning: From Algorithms to Applications or
• 6.7810 Algorithms for Inference or
• 6.8610 (6.864) Advanced Natural Language Processing or
• 6.7900 (6.867) Machine Learning or
• 6.8710 (6.874) Computational Systems Biology: Deep Learning in the Life Sciences or
• 9.520[J] – Statistical Learning Theory and Applications or
• 16.940 – Numerical Methods for Stochastic Modeling and Inference or
• 18.337 – Numerical Computing and Interactive Software
• 8.316 – Data Science in Physics or
• 6.8300 (6.869) Advances in Computer Vision or
• 8.334 – Statistical Mechanics II or
• 8.371[J] – Quantum Information Science or
• 8.591[J] – Systems Biology or
• 8.592[J] – Statistical Physics in Biology or
• 8.942 – Cosmology or
• 9.583 – Functional MRI: Data Acquisition and Analysis or
• 16.456[J] – Biomedical Signal and Image Processing or
• 18.367 – Waves and Imaging or
• IDS.131[J] – Statistics, Computation, and Applications

Grade Policy

C, D, F, and O grades are unacceptable. Students should not earn more B grades than A grades, reflected by a PhysSDS GPA of ≥ 4.5. Students may be required to retake subjects graded B or lower, although generally one B grade will be tolerated.

Unless approved by the PhysSDS co-chairs, a minimum grade of B+ is required in all 12 unit courses, except IDS.190 (3 units) which requires a P grade.

Though not required, it is strongly encouraged for a member of the MIT  Statistics and Data Science Center (SDSC)  to serve on a student’s doctoral committee. This could be an SDSC member from the Physics department or from another field relevant to the proposed thesis research.

Thesis Proposal

All students must submit a thesis proposal using the standard Physics format. Dissertation research must involve the utilization of statistical methods in a substantial way.

PhysSDS Committee

• Jesse Thaler (co-chair)
• Mike Williams (co-chair)
• Isaac Chuang
• Janet Conrad
• William Detmold
• Philip Harris
• Jacqueline Hewitt
• Kiyoshi Masui
• Leonid Mirny
• Christoph Paus
• Phiala Shanahan
• Marin Soljačić
• Washington Taylor
• Max Tegmark

Can I satisfy the requirements with courses taken at Harvard?

Harvard CompSci 181 will count as the equivalent of MIT’s 6.867.  For the status of other courses, please contact the program co-chairs.

Can a course count both for the Physics degree requirements and the PhysSDS requirements?

Yes, this is possible, as long as the courses are already on the approved list of requirements. E.g. 8.592 can count as a breadth requirement for a NUPAX student as well as a Data Analysis requirement for the PhysSDS degree.

If I have previous experience in Probability and/or Statistics, can I test out of these requirements?

These courses are required by all of the IDPS degrees. They are meant to ensure that all students obtaining an IDPS degree share the same solid grounding in these fundamentals, and to help build a community of IDPS students across the various disciplines. Only in exceptional cases might it be possible to substitute more advanced courses in these areas.

Can I substitute a similar or more advanced course for the PhysSDS requirements?

Yes, this is possible for the “computation and statistics” and “data analysis” requirements, with permission of program co-chairs. Substitutions for the “probability” and “statistics” requirements will only be granted in exceptional cases.

For Spring 2021, the following course has been approved as a substitution for the “computation and statistics” requirement:   18.408 (Theoretical Foundations for Deep Learning) .

The following course has been approved as a substitution for the “data analysis” requirement:   6.481 (Introduction to Statistical Data Analysis) .

Can I apply for the PhysSDS degree in my last semester at MIT?

No, you must apply no later than your penultimate semester.

What does it mean to use statistical methods in a “substantial way” in one’s thesis?

The ideal case is that one’s thesis advances statistics research independent of the Physics applications. Advancing the use of statistical methods in one’s subfield of Physics would also qualify. Applying well-established statistical methods in one’s thesis could qualify, if the application is central to the Physics result. In all cases, we expect the student to demonstrate mastery of statistics and data science.

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PhD in Data Science and Analytics

Degrees & Programs

• Doctoral Degree in Data Science and Analytics
• Certificates

We launched the first formal PhD program in Data Science in 2015.  Our program sits at the intersection ofcomputer science, statistics, mathematics, and business.  Our students engage in relevant research with faculty from across our eleven colleges.  As one of the institutions on the forefront of the development of data science as an academic discipline, we are committed to developing the next generation of Data Science leaders, researchers, and educators. Culturally, we are committed to the discipline of Data Science, through ethical practices, attention to fairness, to a diverse student body, to academic excellence, and research which makes positive contributions to our local, regional, and global community.   

Herman Ray , Director, Ph.D. in Data Science and Analytics

About the Doctoral Degree in Data Science and Analytics

This degree will train individuals to translate and facilitate new innovative research, structured and unstructured, complex data into information to improve decision making. This curriculum includes heavy emphasis on programming, data mining, statistical modeling, and the mathematical foundations to support these concepts. Importantly, the program also emphasizes communication skills – both oral and written – as well as application and tying results to business and research problems.

Because this degree is a Ph.D., it creates flexibility. Graduates can either pursue a position in the private or public sector as a "practicing" Data Scientist – where continued demand is expected to greatly outpace the supply - or pursue a position within academia, where they would be uniquely qualified to teach these skills to the next generation.

Information Sessions for Fall 2025 Admission

To be announced

Data Science and Analytics PhD Curriculum

Stage One: Pre-Program Requirements

• Successful applicants will have completed a masters degree in a computational field (e.g., engineering, computer science, statistics, economics, finance, etc.)
• Applicants are expected to have deep proficiency in at least one analytical programming language (e.g., SAS, R, Python). SQL and Java are helpful but not required.
• Interested applicants who have earned an undergraduate degree are encouraged to apply to the Ph.D. Program with the embedded MS in Computer Science or with the MS in Applied Statistics.

Stage Two: Coursework

The Ph.D. in Data Science and Analytics requires 78 total credit hours spread over four years of study. Example Program of Study: 

• CS 8265  - Big Data Analytics
• CS 8267  - Machine Learning
• MATH 8010  - Theory of Linear Models (optional)
• MATH 8020  - Graph Theory
• MATH 8030  - Applied Discrete and Combinatorial Mathematics 
• STAT 8240  - Data Mining I
• STAT 8250  - Data Mining II
• Comprehensive Exam 
• 21 credit hours of electives/concentration

Students take up to 9 credit hours of 6000- or 7000-level courses in DS, STAT, or CS with permission of the program director. Students take any 8000- or 9000-level course in DS, STAT, MATH, CS or IT, or the HHS courses in the mHealth concentration.

• at least 15 credit hours in CS courses at 8000 or 9000 levels (except CS 9900)
• at least 15 credit hours in STAT courses at 8000 or 9000 levels
• HHS 8000 - Introduction to mHealth
• HHS 8010 - Ethical Issues in mHealth, Healthcare and Human Subjects Research
• STAT 8235 - Advanced Longitudinal Data Analysis
• HHS 8050 - Advanced Research in mHealth
• HHS 8020 - mHealth Applications or HHS 8030 - Advanced Special Topics in mHealth
• Develop Dissertation Research Proposal 
• DS 9700 Doctoral Internship/Research Lab
• DS 9900 Dissertation
• Dissertation Proposal Defense
• DS 9900 DissertationFinal Dissertation Defense

Stage Three: Project Engagement and Research/Dissertation

Relevant, interdisciplinary research forms the foundation of the Ph.D. in Data Science and Analytics. While students are encouraged to engage in research from their first semester, the last two years of the program are structured to help students transition into becoming independent, lead researchers. In this last stage of the program, students will work with research faculty, including their advisor, in one of our data science research labs.

Program Student Learning Outcomes

At the end of the program, students will be able to:

• Demonstrate their understanding of the research process
• Demonstrate mastery of core concepts relevant to three key areas in mathematics, statistics and computer science
• Develop themselves as professionals prepared for work as a doctoral-educated individual beyond graduation

Admission Requirements and Application

Frequently Asked Questions (FAQ)

How long will the program take?

How much does the program cost?

Who would be successful in the program?

Where do these graduates work after graduation?

What are the publication/research requirements?

What did Science Doctoral Students Study?

• Applied Computer Science
• Applied Economics and Statistics
• Applied Statistics
• Applied Mathematics
• Bioinformatics
• Business Analytics
• Chemical Biology
• Computer Science
• Data Science
• Forecasting & Strategic Management
• Integrative Biology
• Public Admin in Economic Policy Mgmt
• Mathematics
• Mechanical Engineering
• Software Engineering

What is the Project Engagement requirement?

Can I pursue the program part- time while I am working full-time?

Can I live on campus?

Are the courses online?

Do I have to have a masters degree to apply?

Where did Data Doctoral Students Study?

• Ajou University, South Korea
• Albert-Ludwigs University of Freiburg
• Auburn University
• Bowling Green State University
• Clemson University
• Columbia University
• Columbus State University
• Florida State University
• Georgia Southern University
• Georgia State
• Georgia Tech
• Iran University of Science and Technology
• Kennesaw State University
• Marshall University
• Michigan State University
• Murray State University
• North Carolina State University
• St. Petersburg State University, Russia
• University of KwaZulu-Natal, South Africa
• University of Michigan
• University of North Carolina
• University of Toledo

Ph.D. in Data Science and Analytics Student Cohorts

Royce Alfred

Bachelor's Degree:   Psychology, Kennesaw State University

Master's Degree:   Applied Statistics and Analytics, Kennesaw State University

Work History:   4 years as a Data Scientist at Equifax

Professional Objective:   Work as a research data scientist in the corporate environment

Venkata Abhiram Chitty

Bachelor's Degree:   Mathematics, Statistics and Computer Science, Osmania University, Telangana, India

Master's Degree:   Data Science, VIT-AP University, Amaravati, Andhra Pradesh, India

Professional Objective:   To apply my Data Science skills in public health domain and help the society

Caleb Greski

Bachelor's Degree: 

Master's Degree: 

Work History: 

Courses Taught: 

Publications: 

Professional Objective: 

Moukthika Kadaparthi

Bachelor's Degree:   Electrical and Electronics Engineering, SASTRA Deemed University

Master's Degree:   Computers and Information Science, Cleveland State University

Work History:  

• Business Intelligence Analyst, Philips Healthcare, Georgia
• Graduate Research Assistant, Cleveland State University, Ohio 

Professional Objective:   My objective is to enter academia with the aim of sharing the practical applications of data science in diverse domains and its potential positive impacts. With my unique blend of academic rigor and industry experience, I am driven to analyze complex data sets using cutting-edge data science techniques, to provide actionable insights and support data-driven decision-making.

Bachelor's Degree:   Civil Engineering, Huazhong University of Science and Technology, China

Master's Degree:   Business Analytics, Syracuse University

• Credit Modeling Analyst, Agricultural Development Bank of China
• Research Assistant, Changjiang Securities
• Graduate Assistant, Syracuse University

Courses Taught:  Calculus I, Marketing Analytics, Data Mining

Awards:   Merit-Based Scholarship, Syracuse University

Professional Objective:   To secure a challenging position in a reputable organization to expand myself within the field of Artificial Intelligence.

Kausar Perveen

Bachelor's Degree:   Bachelor in Engineering Software Engineering, National University of Sciences and Technology, Pakistan

Master's Degree:   Masters in Data Science, Illinois Institute of Technology, Chicago

• Fullstack Developer at ItRunsInMyFamily, Charleston, South Carolina
• Software Engineer II , Xgrid Pakistan
• Senior Research Coordinator, Aga Khan University Pakistan
• Machine Learning Engineer, Agoda Thailand

Publications:  National cervical cancer burden estimation through systematic review and analysis of publicly available data in Pakistan 

Service and Awards:

• Fulbright Scholarship award for Master’s degree in Data Science
• Aga Khan Education Service Pakistan, merit cumulative need based scholarship for Bachelors in Software Engineering 

Professional Objective:  My main motivation behind getting a degree in Data Science is to receive and perform qualified research experience in Data Science and public health

Bachelor's Degree:   Statistics, University of Dhaka, Dhaka, Bangladesh

Master's Degree:   Mathematics (Statistics Concentration), University of Toledo, Ohio

• Analytics Engineer Intern, Cooper Smith, Toledo, Ohio
• Business AnalystAkij Food and Beverage Limited, Dhaka, Bangladesh

Courses Taught:   Introduction to Statistics

Professional Objective:   I am interested to work as a data scientist in the industry

Ayomide Isaac Afolabi

Bachelor's Degree:  Chemical Engineering, Ladoke Akintola University of Technology 

Master's Degree:  Data Science, Auburn University 

Work History:   Graduate Research Assistant, Auburn University 

Courses Taught:   Python Programming 

Publications:   Larson EA, Afolabi A, Zheng J, Ojeda AS. Sterols and sterol ratios to trace fecal contamination: pitfalls and potential solutions. Environ Sci Pollut Res Int. 2022 Jul;29(35):53395-53402.  doi: 10.1007/s11356-022-19611-2 . Epub 2022 Mar 14. PMID: 35287190

Professional Objective:  To work as a research data scientist in the industry

Dinesh Chowdary Attota

Bachelor's Degree:   Computer Science, Jawaharlal Nehru Technological University Kakinada (JNTUK), India

Master's Degree:   Computer Science, Kennesaw State University

Work History:   Associate Consultant, SL Techknow Solutions India Pvt Ltd, India  2018 - 2020

Publications:  

• An Ensemble Multi-View Federated Learning Intrusion Detection for IoT
• A Conversational Recommender System for Exploring Pedagogical Design Patterns
• An Ensembled Method For Diabetic Retinopathy Classification using Transfer Learning  

Professional Objective:   I'd like to be a faculty member at a university so that I can continue to do research.

Nzubechukwu Ohalete

Bachelor's Degree:   Mathematics,University of Nigeria, Nsukka

Master's Degree:   Applied Statistics, Bowling Green State University

Work History:   Graduate Assistant/Data Analyst, Federal University of Technology, Owerri - Mathematics Department

Courses Taught:  Elementary Mathematics, Mathematical Methods

Awards:   James A. Sullivan Outstanding Graduate Student Award, Applied Statistics and Operations Research Department, April 2022

Professional Objective:   To use data science techniques to solve problems which makes our lives better and also makes our world a better place

Ryan Parker

Bachelor's Degree:  Microbiology, University of Tennessee - Knoxville

Master's Degree:   Integrative Biology, Kennesaw State University

Work History:  Instructor of Biology, Kennesaw State University

Courses Taught:   Nursing Microbiology Lectures and Labs, Introductory Biology Labs, Biotechnology Lectures and Labs

• Parker RA, Gabriel KT, Graham K, Cornelison CT. Validation of methylene blue viability staining with the emerging pathogen Candida auris. J Microbiol Methods. 2020 Feb;169:105829.   doi: 10.1016/j.mimet.2019.105829 . Epub 2019 Dec 27. PMID: 31884053.
• Parker RA, Gabriel KT, Graham KD, Butts BK, Cornelison CT. Antifungal Activity of Select Essential Oils against Candida auris and Their Interactions with Antifungal Drugs. Pathogens. 2022 Jul 22;11(8):821.   doi: 10.3390/pathogens11080821 . PMID: 35894044; PMCID: PMC9331469.

Awards:   Best Graduate Poster: Symposium for Student Scholars hosted by Kennesaw State University (Fall 2018) for Poster: "Antifungal Activity of Select Essential Oils and Synergism with Antifungal Drugs against Candida auris"

Professional Objective : To apply Data Science techniques to large scientific datasets, such as genomic and astronomical data, and to help bridge the gap between disparate fields by working in an interdisciplinary space to offer integrative and data-driven solutions to the increasingly complex problems presented to the traditional Sciences.

Askhat Yktybaev

Bachelor's Degree:   Forecasting and Strategic Management, Saint-Petersburg State University of Economics and Finance, Russia

Master's Degree:   Forecasting and Strategic Management, Saint-Petersburg State University of Economics and Finance, Russia; Public Administration in Economic Policy Management, School of International and Public Affairs, Columbia University

Work History:

• from Data Analyst to Head of Research Unit, Central Bank of Kyrgyz Republic
• Sr. Data Scientist in OJSC, Aiyl Bank, Kyrgyzstan
• Consultant, The World Bank, Washington D.C.

Courses Taught:   Financial Programing in the Central Bank, Monetary Policy Transmission Mechanism

Service and Awards:   Winner of the Joint Japan/World Bank Graduate Scholarship Program, National Bank Silver Medal for Best Forecast

Professional Objective:   I want to found a successful Fintech startup one day.

Sanad Biswas

Bachelor's Degree:   Statistics, Biostatistics and Informatics, University of Dhaka, Bangladesh

Master's Degree:   Statistics, University of Toledo, OH

• Research Assistant: US Army Research Lab, Kennesaw State University
• Consultant, Statistical Consulting Service, University of Toledo
• Graduate Teaching Assistant, University of Toledo

Courses Taught:   Calculus and Business Calculus, Facilitated students’ study of Statistics courses at the University of Toledo.

Professional Objective:   To work as a researcher in the industry or as a faculty. I am primarily interested in the application of machine learning in different fields.

Mallika Boyapati

Bachelor's Degree:  Electronics and Computer Engineering, K L University, India

Master's Degree:  Applied Computer Science, Columbus State University

• T-Mobile, Seattle, WA, USA: Sr. Data analyst, 2018- 2021
• UITS, Columbus State University, Columbus, GA, USA: Data Analyst -Graduate assistant, 2016-2018
• Menlo Technologies, India: Jr. Data Analyst, Intern, 2014- 2016

Courses Taught:   DATA 4310 - Statistical Data Mining

Publications:

• Anti-Phishing Approaches in the Era of the Internet of Things. In: Pathan, AS.K. (eds) Towards a Wireless Connected World: Achievements and New Technologies. Springer, Cham -   https://doi.org/10.1007/978-3-031-04321-5_3
• An empirical analysis of image augmentation against model inversion attack in federated learning -   https://doi.org/10.1007/s10586-022-03596-1
• M. Boyapati and R. Aygun, "Phishing Web Page Detection using Web Scraping," SoutheastCon 2023, Orlando, FL, USA, 2023, pp. 167-174, doi: 10.1109/SoutheastCon51012.2023.10115148.
• M. Boyapati and R. Aygun, "Default Prediction on Commercial Credit Big Data Using Graph-based Variable Clustering," 2023 IEEE 17th International Conference on Semantic Computing (ICSC), Laguna Hills, CA, USA, 2023, pp. 139-142, doi: 10.1109/ICSC56153.2023.00029.
• Boyapati, M., Aygun, R. (2023) Explainable Machine Learning for Default Prediction on Commercial Credit Big Data Using Graph-based Variable Clustering. In Encyclopedia with Semantic Computing and Robotic Intelligence VOL. 0 https://doi.org/10.1142/S2529737623500119
• Winners of Dataiku March Madness Bracket-thon, 2021 in predicting the NBA bracket
• Winners of 2021 Analytics Day Ph.D. level research poster presentation 

Professional Objective:   To leverage strong analytical and technical abilities to research and develop effective data models, visualize data, and uncover insights that makes an impact in field of data science

Nina Grundlingh

Bachelor's Degree:   Applied Mathematics and Statistics, University of KwaZulu-Natal, South Africa

Master's Degree:   Statistics, University of KwaZulu-Natal, South Africa

Courses Taught:   Introduction to Statistics, University of KwaZulu-Natal

• Grundlingh, N., Zewotir, T., Roberts, D. & Manda, S. Modelling diabetes in South Africa. The 61st conference of the South African Statistical Association, 27-29 November 2019, Nelson Mandela University, South Africa.
• Grundlingh, N., Zewotir, T., Roberts, D. & Manda, S. Modelling diabetes in the South African population. College of Agriculture, Engineering and Science Postgraduate Research & Innovation Symposium 2019, 17 October 2019, University of KwaZulu-Natal, Westville, South Africa (the award for best MSc presentation was also received for this).
• Grundlingh, N., Zewotir, T., Roberts, D. & Manda, S. Modelling risk factors of diabetes and pre-diabetes in South Africa. IBS SUSAN-SSACAB 2019 Conference, 8-11 September 2019, Cape Town, South Africa.
• University of KwaZulu-Natal Postgraduate Research & Innovation Symposium 2019 – Best Masters oral presentation
• South African Statistical Association Honours Project Competition 2018/2019 – 2nd place and special prize for best use of SAS

Professional Objective:   To work in a teaching position – sharing how data science can be applied to different fields and the positive impact it could have. I would like to use my theological background and passion to bring insight, clarity, and wisdom to data science problems. 

Namazbai Ishmakhametov

Bachelor's Degree:   Specialist in Mathematical Methods in Economics, Kyrgyz-Russian Slavic University

Master's Degree:   Analytics, Institute for Advanced Analytics at North Carolina State University

• Expert at the Centre for Economic Research, National bank of the Kyrgyz Republic
• Consultant in World Bank project dedicated to strengthening the regulatory practices in Kyrgyz Republic
• Consultant at Deloitte Consulting LLP, Science Based Services group, Analytics & Cognitive offering
• Macroeconomic modeling expert in the Economic Department, National bank of the Kyrgyz Republic

Courses Taught:   Introductory statistics and econometrics (cross-sections, times series and panels) lecturer at Ata-Turk Alatoo International University, Kyrgyzstan

• Ishmakhametov Namazbai, Abdygulov Tolkunbek, Jenish Nurbek. 2020. “ Impact of 2014-2015 shocks on economic behavior of the households in the Kyrgyz Republic ". Working Paper of the National Bank of the Kyrgyz Republic
• Sherrill W. Hayes, Jennifer L. Priestley, Namazbai Ishmakhametov, Herman E. Ray. 2020. “ I’m not Working from Home, I’m Living at Work ”: Perceived Stress and Work-Related Burnout before and during COVID-19”. PsyArxiv Preprints
• Ishmakhametov Namazbai, Arykov Ruslan. 2016. “ Credit Risk Model on the Example of the Commercial Banks of the Kyrgyz Republic ”. Working Paper of the National Bank of the Kyrgyz Republic
• Namazbai Ishmakhametov, Anvar Muratkhanov.2015. “Modeling strategy of the Bank of the Kyrgyz Republic”. National bank of Poland – Swiss National bank joint seminar. Zurich, Switzerland

Professional Objective:   To apply my quantitative skills in the field of biotech either in corporate or government sector

Symon Kimitei

Bachelor's Degrees:   Mathematics, Kennesaw State University, and Computer Science,  Kennesaw State University

Master's Degree:   Mathematics (Scientific Computing Concentration), Georgia State University 

Work History:   Senior Lecturer and Math Department Coordinator of Supplemental Instruction, Kennesaw State University

Courses Taught:   Calculus 1, Precalculus, Applied Calculus & College Algebra 

• Haskin, S., Kimitei, S., Chowdhury, M., Rahman, F., Longitudinal Predictive Curves of Health-Risk Factors for American Adolescent Girls. Journal of Adolescent Health.  JAH-2021-00601R1
• Symon K Kimitei,   Algorithms for Toeplitz Matrices with Applications to Image Deblurring . 2008. Georgia State University, Masters thesis. ScholarWorks 

Poster Presentations:

• Kimitei, Symon & Sammie Haskin. "Nadaraya-Watson Kernel Regression Longitudinal Analysis of Healthcare Risk Factors of African American and Caucasian American Girls." Kennesaw State University R Day Presentation.  11 Nov. 2019. Poster presentation.
• Kimitei, Symon. " Social Network Analysis in Supreme Court Case Rulings by Precedence Using SAS Optgraph/Python." 23rd Annual Symposium of Scholars. Kennesaw State University.  19 April. 2018. Poster presentation.

Professional Objective:   As a Ph.D. student in Analytics & Data Science, I hope to gain skills in the program that will propel me into a Data Scientist / Machine Learning Engineer with a specialization in the design and implementation of deep learning & machine learning algorithms.

Jitendra Sai Kota

Bachelor's Degree:   Computer Science & Engineering, Amrita Vishwa Vidyapeetham, India

Master's Degree:   Computer Science, Florida State University

Work History:   Teaching Assistant Professor in Computer Science at an Engineering College in India

Courses Taught:   Problem Solving & Program Design through C, Artificial Intelligence, Data Mining

Publications:  Kota, Jitendra Sai, Vayelapelli, Mamatha. 2020. "Predicting the Outcome of a T20 Cricket Game Based on the Players' Abilities to Perform Under Pressure". IEIE Transactions on Smart Processing and Computing 9(3):230-237.   DOI: 10.5573/IEIESPC.2020.9.3.230

Professional Objective:   to work in Data Science in a Corporate Environment

ResearchGate

Catrice Taylor

Bachelor's Degree:   Economics, Clemson University 

Master's Degrees:  Applied Economics and Statistics, Clemson University, and Applied Statistics, Kennesaw State University 

Professional Objective:   To work as an industry data scientist in a corporate environment 

Sahar Yarmohammadtoosky

Bachelor's Degree:   Applied Mathematics, Sheikh Bahaei University, Isfahan, Iran 

Master's Degree:   Applied Mathematics, Iran University of Science & Technology, Tehran, Iran

Courses Taught:  Numerical Analysis and Linear Algebra, Iran University of Science & Technology

Publications:   Noah, G., Sahar, Y., Anthony P. & Hung, C.C. "ISODS: An ISODATA-Based Initial Centroid Algorithm". Accepted to: 10th International Conference on Information, March 6 - 8, 2021, Hosei University, Tokyo, Japan

Professional Objective:   My goal is to become a competent Data Science specialist capable of using my skills to bring meaning to data, getting a faculty position at a university

Martin Brown

Graduation Date: Spring 2024

Dissertation: A Holistic and Collaborative Behavioral Health Detection Framework Using Sensitive Police Narratives

Dissertation Advisors: Dr. Dominic Thomas and Dr. Md Abdullah Al Hafiz Khan

Inchan Hwang

Bachelor’s Degree: Computer Science, Georgia Southwestern State University

Master’s Degree: Software Engineering, Ajou University, South Korea

Courses Tutored: Precalculus, College Algebra, Calculus I at Georgia Southwestern State University

Tutoring College Algebra, Calculus I and II at Academic Skills Center, Georgia Southwestern State University Research Assistant at Intelligence of HyperConnected Systems Lab of Ajou University Fullstack web developer, windows system programmer in the cybersecurity industry Professional Objective: To work in big data analytics, and research and development of machine learning in engineering, and security

Duleep Prasanna Rathgamage Don

Bachelor's degree:   Physics and Mathematics, The Open University of Sri Lanka

Master's degree:   Mathematics, Georgia Southern University

• Graduate Teaching Assistant, Georgia Southern University, 2016 - 2018
• Graduate Teaching Assistant, University of Wyoming, 2019 - 2020

Courses Taught:   Trigonometry, and Calculus I & II

Publications/Presentations:

• Don, R. D. and Iacob, I. E., ‘DCSVM: Fast Multi-class Classification using Support Vector Machines’,   International Journal of Machine Learning and Cybernetics .
• Rathgamage Don, D., Iacob, E., ‘Divide and Conquer Support Vector Machine for Multiclass Classification’, Research Symposium (2018), Georgia Southern University.
• Rathgamage Don, D., Iacob, E., ‘Multiclass Classification using Support Vector Machines’, MAA Southeastern Section Meeting (2018), Clemson University.

Professional Objective:   To work in big data analytics, and research and development of machine learning in engineering, and medicine

Linglin Zhang

Bachelor’s Degree:   Biological Sciences, Hubei University, China

Master’s Degree:   Chemical Biology, University of Michigan and Bioinformatics, Georgia Institute of Technology

Selected Publications:   Rebecca Shen, Zhi Li, Linglin Zhang, Yingqi Hua, Min Mao, Zhicong Li, Zhengdong Cai, Yunping Qiu, Jonathan Gryak, Kayvan Najarian. (2018). Osteosarcoma Patients Classification Using Plain X-Rays and Metabolomic Data. 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 690-693, 2018.

Professional Objective:  To become a researcher in industry or academia. My background in Biology and Bioinformatics could provide me strong theoretical support on a research role in the health industry. The experience of doing an internship at Equifax equipped me of certain knowledge on business cases. 

Yihong Zhang

Bachelor’s Degree:   Psychology Mathematics Interdisciplinary, Chatham University

Master’s Degree:   Mathematics and Statistics Allied with Computer Science, Georgia State University

• Research Assistant - Collaborated with biomedical department to analyze and visualize microarray gene expression data, Facilitated in data pre-processing and machine learning modeling of clinical liver cirrhosis image data, Assisted in feature engineering of image analysis in deep learning for pathology diagnosis with Mayo Clinic’s pilot project.
• Graduate Lab Assistant - Tutored students with statistics and math subjects.

Professional Objective:   Make better use of data in healthcare and bioinformatic industry as a data scientist.

2019 - 2020

Trent Geisler

Graduation Date:   Summer 2022

Dissertation:   Novel Instance-Level Weighted Loss Function for Imbalanced Learning

Dissertation Advisor:   Dr. Herman Ray

Current Position:   Assistant Professor, Department of Systems Engineering, United States Military Academy West Point

Srivatsa Mallapragada

Dissertation: Multi-Modality Transformer for E-Commerce: Inferring User Purchase Intention to Bridge the Query-Product Gap

Dissertation Advisor: Dr. Ying Xie

Current Position: Data Scientist, Rue Gilt Groupe (RGG)

Sudhashree Sayenju

Graduation Date:   Spring 2023

Dissertation:   Quantification and Mitigation of Various Types of Biases in Deep NLP Models

Dissertation Advisor:   Dr. Ramazan Aygun

Current Position: Lecturer, Data Science and Analytics, Kennesaw State University

Christina Stradwick

Bachelor’s Degree:  Music Performance and Mathematics, Marshall University

Master’s Degree:  Mathematics with Emphasis in Statistics, Marshall University

Courses Taught:  Prep for College Algebra at Marshall University

Selected Presentations:

• Stradwick, C. Exploring the Variance of the Sample Variance. Spring Meeting of the Mathematical Association of America Ohio Section, University of Akron, 2019.
• Stradwick, C., Vaughn, L., Hanan Khan, A. Data Modeling on Insurance Beneficiary Dataset. College of Science Research Expo 2018, Marshall University, 2018. Poster Presentation.
• Stradwick, C. Disease modeling on networks. The 13th Annual UNCG Regional Mathematics and Statistics Conference, University of North Carolina at Greensboro, 2017. Poster Presentation.

Professional Objectives:  To work as a researcher in industry or in a laboratory setting. I would like to use my background in mathematics and statistics to develop novel solutions that address limitations in current data science techniques and to apply known data science methods to solve real-world problems.

2018 - 2019

Md Shafiul Alam

Graduation Date:   Fall 2022

Dissertation:   Appley:   App roximate Shap ley   Values for Model Explainability in Linear Time

Dissertation Advisor:   Dr. Ying Xie

Current Position:   AI Framework Engineer, Intel Corporation

Jonathan Boardman

Dissertation:   Ethical Analytics: A Framework for a Practically-Oriented Sub-Discipline of AI Ethics

Current Position:   Data Scientist, Equifax

Tejaswini Mallavarapu

Bachelor’s Degree:   Pharmacy, Acharya Nagarjuna University, India

Master’s Degree:   Computer Science, Kennesaw State University

• Graduate Research Assistant, Kennesaw State University, 2017-present
• Research Analyst, Divis Laboratories, 2013-2014

Selected Publications:

• T. Mallavarapu, Y. Kim, J.H. Oh, and M. Kang, "R-PathCluster: Identifying Cancer Subtype of Glioblastoma Multiforme Using Pathway-Based Restricted Boltzmann Machine," Proceedings of IEEE International Conference on Bioinformatics & Biomedicine (IEEE BIBM 2017), International Workshop on Deep Learning in Bioinformatics, Biomedicine, and Healthcare Informatics, Accepted, 2017.
• M.R. Shivalingam, K.S.G. Arul Kumaran, D. Jeslin, Ch. MadhusudhanaRao, M. Tejaswini, "Design and Evaluation of Binding Properties of Cassia roxburghii Seed Galacto mannan and Moringa oleifera Gum in the Formulation of Paracetamol Tablets," Research Journal of Pharmacy and Technology(RJPT). 3(1): Jan.-Mar. 2010; Page 254-256.
• M.R. Shivalingam, K.S.G. Arul Kumaran, D. Jeslin, Y.V. Kishore Reddy, M. Tejaswini, Ch. MadhusudhanaRao, V. Tejopavan, "Cassia roxburghii Seed Galacto manna— a potential binding agent in the tablet formulation," Journal of Biomedical Science and Research(JBSR), Vol 2 (1), 2010, 18-22

Professional Objective:   To be a data scientist in the field of health care or bioinformatics where I can leverage my analytical skills and knowledge towards the advancement of the research field.

Seema Sangari

Dissertation:   Debiasing Cyber Incidents - Correcting for Reporting Delays and Under-reporting

Dissertation Advisor:   Dr. Michael Whitman

Current Position:   Principal Modeler, HSB 

Srivarna Settisara Janney

Bachelor’s Degree:   Mechanical Engineering, Visveswaraiah Technological University, India

• Graduate Research Assistant, Kennesaw State University, 2016-2018
• Senior Software Engineer, Torry Harris Business Solutions (THBS), United Kingdom, 2010-2012 and India, 2012-2014
• Software Engineer, Torry Harris Business Solutions (THBS), India, 2007-2010

Selected Publications/Presentations:

• S.S. Janney, S. Chakravarty, “New Algorithms for CS – MRI: WTWTS, DWTS, WDWTS”, One-page research paper, 40th International Conference of IEEE Engineering in Medicine and Biology Society (IEEE EMBC), Jul 2018
• Master thesis presented at Southeast Symposium on Contemporary Engineering Topics (SSCET), UAH Engineering Forum, Alabama, Aug 2018
• Master thesis poster is accepted to be presented at Biomedical Engineering Society (BMES) 2018 Annual Meeting, Oct 2018
• Submitted draft copy for book chapter contribution on “Bioelectronics and Medical Devices”, Elsevier Publisher, May 2018
• Showcased 3MT, Georgia Council of Graduate Schools (GCGS), Apr 2018
• Master thesis presented in workshop for “Medical Signal and Image Processing” at Department of Biotechnology & Medical Engineering, NIT Rourkella, Feb 2018
• S.S. Janney, I. Karim, J. Yang, C.C Hung, Y. Wang, “Monitoring and Assessing Traffic Safety Using Live Video Images”, GDOT project showcase, 4th Annual Transportation Research Expo, Sept 2016
• 1st Place Winner, Graduate Research Project, C-day Poster Presentation, Kennesaw State University, Spring 2018
• People's Choice Award, 3 Minute Thesis (3MT), Apr 2018
• CCSE Dean’s 4.0 Club, Jan 2018
• 3rd Place Winner, Hackathon 2017 - HPCC Systems Big Data
• Foundation of Computer Science, Certified by Kennesaw State University, Jun 2016
• Fundamental of RESTful API Design, Certified by APIGEE, Nov 2014
• Member of HandsOnAtlanta, since 2014
• SOA Associate, Certified by IBM, Jun 2008

Professional Objective:   I would like to be a researcher in Data Science and Analytics in medical imaging technologies contributing to advancements that would help medical and healthcare professionals provide value-based and personalized health care. I would like to look at career opportunities in industry and academia that fuel my interest in research.

2017 - 2018

Graduation Date: Summer 2021

Dissertation: Incentive-based Data Sharing and Exchanging Mechanism Design

Dissertation Advisor: Dr. Meng Han

Current Position: Assistant Professor, Saint Joseph's University - Erivan K. Haub School of Business

Mohammad Masum

Dissertation: Integrated Machine Learning Approaches to Improve Classification Performance and Feature Extraction Process for EEG Dataset

Dissertation Advisor: Dr. Hossain Shahriar

Current Position: Assistant Professor, San Jose State University

Lauren Staples

Graduation Date: Fall 2021

Dissertation: A Distance-Based Clustering Framework for Categorical Time Series: A Case Study in the Episodes of Care Healthcare Delivery System

Dissertation Advisor: Dr. Joseph DeMaio

Current Position: Senior Data Scientist, Microsoft

2016 - 2017

Shashank Hebbar

Dissertation: Tree-BERT - Advanced Representation Learning for Relation Extraction

Current Position: Data Scientist, Credigy

Jessica Rudd

Graduation Date: Summer 2020

Dissertation: Quantitatively Motivated Model Development Framework: Downstream Analysis Effects of Normalization Strategies

Dissertation Advisor: Dr. Herman Ray

Current Position: Senior Data Engineer, Intuit Mailchimp

Graduation Date: Spring 2020

Dissertation: Data-driven Investment Decisions in P2P Lending: Strategies of Integrating Credit Scoring and Profit Scoring

Dissertation Advisor: Dr. Sherry NI

Current Position: Applied Scientist II, Amazon

Dissertation: A Novel Penalized Log-likelihood Function for Class Imbalance Problem

Current Position: Data Scientist/Research Engineer, Hewlett Packard Enterprise

Dissertation: Attack and Defense in Security Analytics

Dissertation Advisor: Dr. Selena He

Current Position: NLP Data Scientist, NBME

2015 - 2016

Edwin Baidoo

Graduation Date:  Spring 2020

Dissertation: A Credit Analysis of the Unbanked and Underbanked: An Argument for Alternative Data

Dissertation Advisor:  Dr. Stefano Mazzotta

Current Position: Assistant Professor, Business Analytics, Tennessee Technological University

Bogdan Gadidov

Graduation Date:  Summer 2019

Dissertation: One- and Two-Step Estimation of Time Variant Parameters and Nonparametric Quantiles

Dissertation Advisor: Dr. Mohammed Chowdhury

Current Position: Data Scientist, Variant

Dissertation:  Biologically Interpretable, Integrative Deep Learning for Cancer Survival Analysis

Dissertation Advisor:  Dr. Mingon Kang

Current Position:  Assistant Professor, Chinese Academy of Medical Sciences, Peking Union Medical College

Graduation Date:  Spring 2019

Dissertation:  Deep Embedding Kernel

Current Position: Assistant Professor, Information Technology, Kennesaw State University

Bob Venderheyden

Graduation Date: Fall 2019

Dissertation:  Ordinal Hyperplane Loss

Dissertation Advisor:  Dr. Ying Xie

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PhD Program

Advanced undergraduate or masters level work in mathematics and statistics will provide a good background for the doctoral program. Quantitatively oriented students with degrees in other scientific fields are also encouraged to apply for admission. In particular, the department has expanded its research and educational activities towards computational biology, mathematical finance and information science. The doctoral program normally takes four to five years to complete.

Doctoral Program in Statistics

Statistics phd minor.

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Data Science, Ph.D.

Data science is an emerging discipline that combines mathematics, computing, and statistics to develop and apply methodologies required for data-driven industries. High demand for data scientists exists in technology, government, and banking. Students graduate prepared to obtain positions in industry using data for decision-making to improve production, reduce costs, and optimize operations.

All data science Ph.D. candidates offered a teaching assistant or research assistant position, if needed

Emphasis on moving from theory to practice in data science for working in interdisciplinary settings involving data-intensive analysis.

Current collaborative research projects applied to finances and Hispanic health disparities in the West Texas Borderplex.

Alum finds new career path in research

After four years teaching high school math, Clarissa Reyes was ready for a career outside the classroom. She returned to UTEP for a master's degree in statistics and was introduced to the Ph.D. in Data Science. The coursework has exposed Clarissa to data scientist careers in research and business. She looks forward to conducting research in clustering techniques and dimension reduction and hopes to work in a national lab after graduation.

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Ph.D. in Data Science

The ph.d. in data science at smu is distinctive because of its highly interdisciplinary nature..

Most existing Data Science Ph.D. programs are either housed in a single department, such as Statistics, Computer Science, Operations Management or Business Analytics; or they focus on a single disciplinary area of research, such as Business or Medicine.

The program’s core curriculum consists of courses in Computer Science, Operations Management, Statistics, and Data Science, and elective courses go beyond those disciplines to include Mathematics, Finance, Marketing, Education, Psychology, Chemistry, Game Design, Economics, and more. Student and faculty interest will continue to set directions for how the program evolves in the future.

Another distinctive feature are the research rotations that students engage in after having completed 4 semesters of coursework.

The goal of this program is to recognize that data science research can inform nearly every discipline at the university and beyond; and that the future of research and work in data science will not be limited to specific and restricted areas.

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Interdisciplinary Doctoral Program in Statistics

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The Interdisciplinary PhD in Statistics (IDPS) is designed for students currently enrolled in a participating MIT doctoral program who wish to develop their understanding of 21st century statistics, using concepts of computation and data analysis as well as elements of classical statistics and probability within their chosen field of study.

Participating programs: Aeronautics & Astronautics Brain and Cognitive Sciences Economics Mathematics Mechanical Engineering Physics Political Science Social and Engineering Systems

How to join IDPS:

Doctoral students in participating programs may submit a selection form between the end of their second semester and penultimate semester in their doctoral program. Selection forms are due by the current semester add date, and students will be notified of a decision by the drop date.

Required documents include a CV, unofficial transcript, anticipated course plan and thesis proposal or statement of interest in statistics.  For access to the selection form or for further information, please contact the IDSS Academic Office at [email protected]

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MIT Statistics + Data Science Center Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge, MA 02139-4307 617-253-1764

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Mathematics, graduate programs.

Mathematics is one of the fundamental, most unified and universal sciences. The discipline of mathematics trains the mind to use logic and reason to discover new knowledge and to make sense of what we already know.

Graduate degrees offered:

M.S. in Mathematics

Ph.D. in Mathematics

M.S. in Applied Mathematics

Ph.D. in Applied Mathematics

M.S. in Statistics and Data Science

Students in our Ph.D. in Applied Mathematics program may also choose to pursue research in Statistics. Our Ph.D. degrees are research-based. There is no research requirement for our M.S. programs.

For more information, call (610) 758-3730 or contact the Graduate Committee at [email protected] .

M.S. in Mathematics, Applied Mathematics, or Statistics and Data Science

The M.S. programs require 30 credit hours of graduate courses with at least 18 hours at the 400 level. With the permission of the chair, up to six hours of these courses can be replaced by a thesis. All M.S. students must also pass a comprehensive examination during their first year which covers calculus, differential equations, and linear algebra (Mathematics or Applied Mathematics) or calculus, linear algebra and probability and statistics (M.S. Statistics and Data Science).

The M.S. degree can serve both as a final degree in mathematics or as an appropriate background for a Ph.D.

Ph.D. in Mathematics or Applied Mathematics

The plan of work for the Ph.D. degree includes the Comprehensive Exam, described above, and Qualifying Examinations. Ph.D. in Mathematics students will all take the Qualifying Examination in the same two areas, Algebra and Real Analysis, within two years. Students in the Ph.D. in the Applied Program will follow a parallel policy and be tested on two different areas, Statistics and Applied Probability for students in the Statistics track and Analysis and Differential Equations for students in the Applied Mathematics track. 

In addition, each student will be required to prepare a written plan for additional advanced course work and substantial reading of research articles. An Advanced Topic examination will also be required in the third year. A General Exam, in which the candidate describes the thesis program, and the doctoral dissertation and its defense complete the work for the PhD degree. A student entering Lehigh without a Masters degree must take 72 credits including at least 48 regular course credits (i.e. not dissertation credits). These students typically earn their Masters degree during the process of their work. A student entering Lehigh with a Masters degree must take 48 credits including at least 18 regular course credits (i.e. not dissertation credits).

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We offer financial support to Ph.D. students through a combination of teaching assistantships, research assistantships, and fellowships.

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PROGRAM DATES: JANUARY 24 – MAY 18, 2022 LOCATION: RESEARCH PROJECTS WILL TAKE PLACE REMOTELY FELLOWSHIP AWARD: $3,500 APPLICATION DEADLINE: OCTOBER 31, 2021

The NYU Center for Data Science

The Center for Data Science (CDS) is the focal point for New York University’s university-wide efforts in Data Science. The Center was established in 2013 to advance NYU’s goal of creating a world-leading Data Science training and research facility, and arming researchers and professionals with the tools to harness the power of Big Data. Today, CDS counts 20 jointly appointed interdisciplinary faculty housed on three floors of our magnificent 60 5th Avenue building, one of New York City’s historic properties. It is home to a top-ranked MS in Data Science program, one of the first PhD programs in Data Science, and a new undergraduate program in Data Science, as well as a lively Fellow and Postdoctoral program. It has over 70 associate and affiliate faculty from 25 departments in 9 schools and units. With cross-disciplinary research and innovative educational programs, CDS is shaping the new field of Data Science.

Data Science for Everyone: Book & Video Series

The universality of data science as a discipline is highlighted with the introduction of a comprehensive textbook and accompanying video series, building upon the foundation laid by the esteemed “Data Science for Everyone” course. These educational materials provide extensive coverage on critical concepts ranging from “how to think like a data scientist” to core subjects such as statistics, programming, and machine learning approaches.

Free Online Resource: Yann LeCun’s Deep Learning Course

Yann LeCun’s Deep Learning Course covers the latest techniques in both deep learning and representation learning, focusing on supervised/self-supervised learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition.

Free Online Resource: Mathematical Tools for Data Science

Mathematical Tools for Data Science, developed by CDS Associate Professor Carlos Fernandez-Granda, provides an introduction to tools from several areas of mathematics such as linear algebra, Fourier analysis, probability theory, and convex optimization, which are useful in data science.

CDS Shines at NeurIPS 2023

Celebrating Success: CDS MS Graduates Forge Impressive Paths in Data Science

Micah Goldblum’s Survey Offers a Deeper Look Into Deep Learning

Hiring Clinical Faculty for 2024: Interview with current CDS Clinical Assistant Professor Louis Mittel

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Math Bachelors -> Data Science PhD / Masters

I'm looking for some advice. I am currently pursuing a B.S. in Applied Mathematics and Physics. However, after a shift of interest, I will be dropping my physics major. I decided to study data science for a year abroad and realized that data science is a career path I want to pursue. I studied introductions to machine learning, data mining, regression, data structures... all using R. Prior to my change in interest, my goal was to pursue a PhD in physics at a top university but now I want to pursue a PhD in data science. I currently am a 6th year student graduating next semester. I currently have a plethora of mathematics and physics courses underneath my belt. I also happen to have co-authored a physics paper in Materials Science and have a decent amount of research experience. My particular areas of research interests (physics and financial engineering) just so happens to be offered at Ivy league schools. In my opinion, I think it is a bit impractical to assume I have a fighting chance at getting offered a PhD position at these universities (Cornell, Columbia, Yale, Stanford, etc...). I do not have a ton of experience in Data Science. Would it be more practical to pursue a masters and obtain more work / research experience before considering a PhD position? I guess an alternative question is: how would I be able to make myself sufficiently competitive to pursue a good Data Science masters / PhD? There are tons of Data Science graduate programs and I'm having some trouble finding applicable ones for me considering graduate application deadlines are around the corner.

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School of Data Science to Launch Ph.D. Program, Formally Joins College of Computing and Informatics

UNC Charlotte’s School of Data Science will soon expand its academic offerings with the establishment of a Doctor of Philosophy in Data Science. Approved by the UNC System Board of Governors in May 2024, the degree program will enroll its first cohort of students in fall 2025, pending approval of the Southern Association of Colleges and Schools Commission on Colleges. 

Established in response to the skyrocketing growth of the data science industry in North Carolina and globally, the new doctorate will offer two pathways for students, providing training for both future industry practitioners and university educators. This transdisciplinary program will emphasize the core technical skills of machine learning, artificial intelligence and statistics along with the social implications and ethics of data use. The program builds on the Master of Science and Bachelor of Science programs offered by the school. Its establishment is the latest example of the University’s commitment to data science, coming over 10 years after the founding of UNC Charlotte’s Data Science Initiative.

 “UNC Charlotte is always working to add and evolve academic programs with an eye toward the future. The creation of the new data science doctoral program is the latest example of our ongoing efforts to align our curriculum to the demands of industry and will allow our thriving School of Data Science to further build on its track record of interdisciplinary innovation,” said Jennifer Troyer, provost and vice chancellor for academic affairs.

The new Ph.D. will become UNC Charlotte’s 25th doctoral degree program.

A new college home and transition in leadership The doctoral program will be enhanced by the School of Data Science formally joining the University’s  College of Computing and Informatics . 

This new structure will support and amplify the school’s ongoing mission to foster interdisciplinary research and partnership across the University, all while providing SDS with the institutional structure necessary for continued faculty expansion, student growth and research excellence as the school continues to bolster its position as an innovative data science institution working to push the field forward. The shift also will allow SDS and CCI to more effectively support the North Carolina General Assembly’s $41.2 million investment toward “Engineering North Carolina’s Future,” in service of the initiative’s call for investing in data science, cybersecurity and engineering efforts across the state.

As part of this transition, the school’s current Director of Research Dongsong Zhang was named its new interim executive director, effective May 15. Zhang is the Belk Endowed Chair in Business Analytics in the Belk College of Business. Founding Executive Director Doug Hague will work closely with Zhang throughout the upcoming academic year during the transition, as Hague begins a new role as executive director of corporate engagement for UNC Charlotte. In his new position, Hague will work hand-in-hand with University leadership, the Division of University Advancement and external partners to build relationships that strengthen UNC Charlotte’s connection with the business community and create additional opportunities for collaboration.

“With the newly approved data science doctoral program and the evolution of the School of Data Science’s relationship with the College of Computing and Informatics, SDS continues to strengthen its position as a leading data science program,” said Bojan Cukic, dean of the College of Computing and Informatics. “I am excited to continue to work alongside Dongsong Zhang in the months ahead as he and his team work to chart the school’s future. We are also extremely grateful for Doug Hague’s bold, thoughtful leadership of SDS over the years. He has played an instrumental role in the school’s incredible success, and we look forward to continued partnership with him in his new role as he works to foster innovation and industry collaboration to the benefit of our University.”

The UNC Charlotte Data Science Initiative was established in 2013. That initiative ultimately grew into the School of Data Science, founded in 2020. The Carolinas’ first School of Data Science, SDS is committed to excellence in education, research and community engagement to shape and lead the future of data science education, research and practice. Since its inception and to this day, the School of Data Science is an interdisciplinary partnership among UNC Charlotte’s College of Computing and Informatics, the Belk College of Business, the College of Humanities & Earth and Social Sciences, the College of Science, the College of Health and Human Services and the William States Lee College of Engineering.

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• Mathematics and Statistics

PhD in Math (specialization in Statistics) or PhD in Data Science

By stxnre December 4, 2023 in Mathematics and Statistics

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I am currently applying to grad school at UCSD, where they have a PhD in Math (specialization in Statistics) and a newer PhD in Data Science. While my original inclination was for the Math PhD, I have last minute started to lean a bit towards the new Data Science program.

While I know more about the Math PhD program, as the department has been there much longer than the Data Science school, I realize that my time there might be very math theory-strong; I still have to take those famous math qual exams in algebra and topology. I still want to understand some theory, as research is an interest, but my aim is not to be a professor. I also know that UCSD has a pretty reputable data science program, so the PhD program, even while not as established, likely will be recognized as a strong program.

One other thing I consider is that some of the faculty in the data science institute are also faculty for the math department.

The deadline for math is Dec 6 (Wednesday), while the deadline for data science is Dec 15 (Friday after next)

If you know anything about UCSD and these departments, or just stats/data science PhD programs in general, your input would be appreciated!

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From their website, it seems like Math PhD students on the stats track would only have to take exams in Mathematical Statistics and Real Analysis, but do you really want to be taking a real analysis qualifying exam at a top 20 math department? Do you want to be wasting your time studying for PhD Math classes?

Personally, given my preferences, it would be a no-brainer to choose the data science program because I don't want to do that much math.  If you don't want to be a professor, I don't see many advantages unless you feel like you would get a lot of personal enjoyment and satisfaction from completing intense math classes.  I'd rather spend that time taking stats/data science electives and getting research experience, so I'd go for the data science PhD.

(There may be some areas/professions where the Math PhD will help you a lot in getting a position - for instance, maybe if you wanted to go into some type of quantitative finance position.  But for biostatistics or technology jobs, I don't think the difference between these programs would matter at all, and I think you could get a lot more research done/learn more practical stuff without the distraction of hard math classes that have nothing to do with statistics)

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The 7 Best Data Science Courses That are Worth Taking in 2024

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A career in data science involves using statistical, computational and analytical methods to extract insights from data. Data scientists regularly use programming languages like Python and R alongside machine-learning algorithms and data-visualisation software.

The need for data scientists has surged across various sectors, including finance, healthcare and technology, making it a highly sought after and lucrative profession. According to the U.S. Bureau of Labor Statistics , the average annual salary for data scientists in 2023 was $108,020, while demand for them is expected to increase by 35% in the next eight years — much faster than average for all occupations.

SEE: What is Data Science? Benefits, Techniques and Use Cases

Online courses and certifications provide accessible pathways into the field, as many can fit around existing responsibilities like a day job. Such programs provide the expertise required for an individual to land their first data science role or just discover whether the career is for them. TechRepublic takes a look at the top six data science courses available in 2024 for learners with different goals and levels of experience.

• Best for a data science overview: IBM Data Science Professional Certificate - Coursera
• Best for beginner Python skills: Associate Data Scientist in Python - DataCamp
• Best for beginner R skills: R Programming A-Z - R For Data Science With Real Exercises! - Udemy
• Best for beginner applications: Applied Data Science Specialization - Coursera
• Best for mathematics for data science: Mathematics for Machine Learning and Data Science Specialization - Coursera
• Best for intermediate applications: Applied Data Science with Python Specialization - Coursera
• Best for college graduates: MITX - Statistics and Data Science with Python - edX

SEE: How to Become a Data Scientist: A Cheat Sheet

Best data science courses: Comparison table

IBM Data Science Professional Certificate - Coursera: Best for a data science overview

The Data Science Professional Certificate from IBM, hosted on Coursera, offers a great starting point for those interested in learning about data science but don’t fully understand what a career in it would entail. This course provides an overview of the tools, languages and libraries used daily by professional data scientists and puts them into practice through a number of exercises and projects. The final Capstone project also requires the student to create a GitHub account, encouraging them to familiarise themselves with the site and collaborate.

$49/£38 per month after a seven-day free trial.

Six months at ten hours a week.

• Industry recognition, as backed by IBM.
• Self-paced.
• Lacks depth, as aims to provide just foundational knowledge of theoretical data science and practical applications.

Pre-requisites

Associate data scientist in python - datacamp: best for beginner python skills.

DataCamp is another well-regarded provider of data-related courses, and one of its highest rated is titled ‘Associate Data Scientist in Python’. It sets itself apart with its unique hands-on coding exercises, one of which involves manipulating and visualising data on Netflix movies. Language-wise, this course exclusively uses Python, but introduces learners to multiple libraries including pandas, Seaborn, Matplotlib and scikit-learn. Knowledge of Python is not required for this course, as the necessary skills are taught along the way.

$13/£11 a month for full access.

Nine weeks at ten hours a week.

• More emphasis on programming skills and data manipulation techniques.
• Taught in Python, the most popular programming language .
• Less depth in theoretical elements of data science.
• Python-specific knowledge may not translate to different environments.

R Programming A-Z - R For Data Science With Real Exercises! - Udemy: Best for beginner R skills

While many data science courses are taught with Python due to its popularity and simplicity, ‘R Programming A-Z’ on Udemy is aimed at learners looking to get to grips with R and RStudio. R is a powerful language used frequently in data science for handling complex data sets. This course assumes no prior knowledge and starts with the very basics of R programming, including variables and for() loops, before looking at matrices, vectors and more advanced data manipulation. Large projects that help cement learning use real-world financial and sports data.

$109.99/£69.99.

10.5 hours of lectures + exercises.

• Specific to R and RStudio.
• Removes the steep learning curve often associated with R.
• Relatively small focus on data science and machine learning.
• Taught on a Mac and instructions for Windows devices are not always clear.

Applied Data Science Specialization - Coursera: Best for beginner applications

“Applied Data Science Specialization,” another course by IBM, fast tracks data science beginners towards skills with real-life applications. Python skills for data analysis and visualisation are taught assuming no prior knowledge of the language and are then put into practice in the interactive labs and projects. These cover the extraction and graphing of financial data, creation of regression models to predict housing prices and visualisation of data treemaps and line plots on Python dashboards. By the end of the course, participants should have solidified their practical Python skills to the extent that they can confidently explore more advanced topics like big data, AI and deep learning.

$49/£38 per month after a seven day free trial.

Two months at ten hours a week.

• Appropriate for beginners.
• Fast tracks learners to practical applications in data science.
• Lack of foundational knowledge provided.

Mathematics for Machine Learning and Data Science Specialization - Coursera: Best for mathematics for data science

As the title suggests, this course from DeepLearning.ai has a particular focus on mathematics for data scientists. Mathematics underpins the profession and is essential for understanding algorithms, cleaning data, drawing insights, visualisation, evaluating models and more. The course covers the fundamental mathematical toolkit of machine learning, including calculus, linear algebra, statistics and probability. Learners say it provides a good entry point into the theory of data science and the lab exercises are practical.

Six weeks at ten hours a week.

• Mathematics covered relevant to applied data science.
• Does not get into lots of depth on each topic.

A high school level of mathematics and a basic knowledge of Python is recommended.

Applied Data Science with Python Specialization - Coursera: Best for intermediate applications

Similar to the IBM “Applied Data Science Specialization” on Coursera, this course does not teach the fundamentals of programming. Instead, it launches straight into applying techniques related to machine learning, data visualisation and text analysis. What differentiates the course is that it is designed for those that already have a basic understanding of Python but want a more in-depth introduction to real-world applications within data science. Key libraries such as Pandas, Matplotlib and Seaborn are used for applied charting, machine learning and text mining. It is led by professors from the University of Michigan via five modules of video lectures, notes and exercises.

Four months at ten hours a week.

• Concentrates on data science applications of Python.
• Requires knowledge of Python.

Background in basic Python or programming required.

MITX - Statistics and Data Science with Python - edX: Best for college graduates

The “Statistics and Data Science with Python” course presented by the Massachusetts Institute of Technology is by far the most comprehensive course featured on this list. The so-called “MicroMasters” takes learners over a year to complete and prepares them for their first career in data science. It provides a graduate-level introduction to concepts such as statistical inference and linear models, as well as practical experience building machine learning algorithms. It is designed to fit around a day job or university study while not compromising on the level of content.

$1,350/£1,186.

One year and two months at ten hours a week.

• Comprehensive.
• Prepares learners for data science jobs.
• Large time commitment required.
• Requires high-level mathematical knowledge,

University-level calculus and comfort with mathematical reasoning and Python programming are recommended.

What is the difference between data analysis and data science?

The key difference between data analysis and data science is that the former primarily looks to interpret existing data, while the latter involves creating new ways of doing so.

Data analysis focuses on examining datasets to identify trends, draw conclusions and support business decisions. It involves cleaning, transforming and modelling data to extract useful information, often using tools like Excel and SQL. It is performed by data analysts who are typically hired into a wide range of industries, including marketing firms, government agencies, healthcare providers, financial institutions and more.

Data science, on the other hand, integrates data analysis with advanced machine learning algorithms, predictive modelling and big data technologies. Data scientists often develop new tools and methods to handle complex problems and derive insights from large-scale datasets. Skills required for this include proficiency in programming languages such as Python and R, as well as a deeper understanding of statistical methods and machine learning.

SEE: 10 Signs You May Not Be Cut Out for a Data Scientist Job

Is data science still in demand in 2024?

Data science remains in high demand in 2024. The IDC estimates that the amount of data worldwide will reach 291 zettabytes by 2027, and as growth continues, more data professionals will be needed to manipulate and interpret it. Furthermore, many of the key industries within which data scientists work are expanding, such as AI, machine learning and the Internet of Things, while others provide core services such as healthcare, energy, finance and logistics. Salaries also reflect this high demand as, according to Glassdoor , the average base pay of a data scientist in the U.S. is $113,000.

Are data science courses worth it?

Opinions on online data science courses vary within the industry. For some, there are enough free resources available through platforms like YouTube to render paid courses unnecessary. They may also argue that there is no substitute for hands-on experience, and that even beginners should learn the necessary skills by downloading an open-source dataset and attempting to manipulate it themselves.

However, the key to learning anything new is persistence, and it can be difficult to remain motivated without a defined learning programme to follow, coursemates to connect with or a course fee at risk of going to waste. For individuals with a tendency to start projects but not finish them, an initial investment in a structured course may provide the motivation they need. Many paid courses also give direct access to qualified instructors who can provide tailored help that would otherwise not be available.

Ultimately, there are certainly opportunities to break into data science without taking any type of online course. However, if structured learning provides the skills or motivation you desire, then the investment may be worth it.

Methodology

When assessing online courses, TechRepublic examined the reliability and popularity of the provider, the depth and variety of topics offered, the practicality of the information, the cost and the duration. The courses and certification programs vary considerably, so be sure to choose the option that is right for your goals and learning style.

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