Computational and Data-Enabled Sciences PhD
The integration of large computing and big data is essential to tackle the urgent research problems in fields ranging from multi-scale modeling and design of materials to natural disasters, smart electric grids, and medical technologies.
We train scientists to analyze big data and multisource datasets to solve these grand challenges, by bringing together the talents of faculty across several departments including the Departments of Mechanical and Aerospace Engineering, Chemical and Biological Engineering, Computer Science and Engineering, Math, Physics, and Chemistry.
The Computational and Data-Enabled Sciences PhD program is an interdisciplinary PhD program that integrates the core areas of data science, numerical algorithms, and high-performance computing toward research and discovery building on a graduate student’s domain science/discipline. Graduate students attending the program are required to have a Master’s degree, which provides the foundation on which the PhD program builds. This foundational Master’s work can be in various disciplines, including but not limited to engineering, mathematics, natural sciences, social sciences, business, and pharmacy. The program aims for a 3-year timeline to completion.
Program Director
Sarah Muldoon
208 Mathematics Building
smuldoon@buffalo.edu
(716) 645-8774
To apply for the Computational and Data-Enabled Sciences PhD program, students should hold a master's degree in a related field, including but not limited to engineering, computer science, mathematics, business, marketing and pharmacy.
Because our program is highly interdisciplinary, many diverse backgrounds will be considered. Applicants must have a 3.0 GPA or better from their bachelor's degree courses and provide the following application materials:
Students can apply online. New applicants must create an account. If you have applied in the past, you will be asked to log in to your existing account to start a new application.
The application includes sections that will ask you to provide us with personal/biographical information, contact information, citizenship details, your past or current college/university, and degree details. You will be able to enter the names and contact information of your recommenders and upload supporting materials to your application. You may upload supporting documents while completing the application form or after you have submitted your application in your Application Status Portal.
You do not need official transcripts or test scores to apply. Unofficial copies are sufficient for application review, but students are encouraged to provide official copies if they have them.
A non-refundable fee of $100 is required to apply for Spring 2025 or later terms. The application fee for Fall 2024 is $85. After you submit your application form, you can pay the application fee online in your Application Status Portal with a credit card or e-check.
Three letters of recommendation are required to apply to this program. Letters are automatically requested when you enter recommenders' names and email addresses in your application. While we will accept letters from professional sources, we strongly prefer letters from professors acquainted with your academic interests, achievements, and abilities.
Copies of transcript(s) for all post-secondary schoolwork must be uploaded with your application for initial review. Upon an offer of admission, accepted applicants will be required to submit official transcripts and proof of degree(s).
Your statement of purpose allows the admissions committee to learn about you as a prospective researcher. There is no minimum or maximum page length or word count.
In the statement, we look for:
- Your ability to present your thoughts in a clear, concise way
- Evidence of your maturity and commitment to conduct research (you will be making contributions of worldwide impact in projects at the cutting edge of their fields)
- Your awareness of advancements in the field and your vision for specific areas where you would like to do research
- Which faculty member(s) your research interests align with or who you identify as potential research mentors
GRE scores are optional for this program. If you would like to take the GRE, you can arrange to take it through the Educational Testing Service (ETS). Official scores should come directly from ETS.
Note: the University at Buffalo Institutional Code is 2925.
International applicants are required to provide proof of English proficiency. The chart below outlines acceptable test types and the university's minimum score required for admission. All applicants whose native language is not English will be required to provide proof of English proficiency.
Copies may be submitted for initial review. Official scores must be sent directly to UB from the testing agency.
| Test Type | University Minimum Score | To Order... |
|---|---|---|
| TOEFL (IBT) (including MyBest scores) TOEFL Home Edition | 79 | Use Institution Code 2925 |
| TOEFL Essentials | 8.5 | Use Institution Code 2925 |
| TOEFL ITP Plus | 550 | Use Institution Code 2925 |
| IELTS and IELTS Indicator | 6.5 | Select "University at Buffalo, State University of New York (SUNY)" |
| 55 | Select "University at Buffalo, State University of New York (SUNY)" | |
| Cambridge English Proficiency (CPE) | 185 | Select "University at Buffalo, State University of New York (SUNY)" |
| Cambridge English Advanced (CAE) | 185 | Select "University at Buffalo, State University of New York (SUNY)" |
| Duolingo English Test (DET) | 120 | ADA Department of Testing will report your official scores to central application services |
Exam results must be dated within two years from your proposed date of admission and remain valid upon entering the term for which you applied.
It is strongly recommended to make test arrangements early in the year so sufficient time can be allowed for the results to be reported before application deadlines.
Exemptions to English Language Proficiency requirements can be found on The Graduate School website.
Application Deadline
We accept applications on a rolling basis throughout the year but encourage students to submit Fall applications by January 1st to be considered for funding opportunities.
The PhD program requires a minimum of 72 credit hours. Additional requirements include passing qualifying exams and preparing your dissertation. Students should refer to the student milestones section of the website to review more about the process of making academic progress as a PhD student.
When you first start our program, you will be assigned an advisor. In the first semester you should select your dissertation committee. Then, you will decide on a research topic and submit a short proposal that articulates your topic and its relationship to the computational science and data science fields. This technical report must be completed no later than the end of the second semester.
We want each dissertation committee to reflect the intellectual diversity of our program. Because of this, the committee:
- should not have more than one core member and one additional member with primary academic affiliation in the same department
- must have core members that are CDES-affiliated faculty
A minimum of 9 credits hours from the approved list of core classes must be taken in each area. One category must have 12 credit hours completed. The core course requirements total 30 credits, with a minimum GPA of 3.2 on a 4.0 scale. Courses taken during your master's program may be transferred and used toward this requirement, with the approval of your dissertation committee and the Graduate Director. We strongly suggest you finish this coursework within the first two years of the program.
The classes below are a list of pre-approved courses that can be taken for each respective category. Students are not limited to these courses, and can work with their primary advisor to identify classes that meet each category requirement. The pre-approved courses should serve as a starting point to help with ideas on what to take!
Data Science
- CSE 574: Intro Machine Learning
- STA 521: Intro Theoretical Statistics 1
- STA 522: Intro Theoretical Statistics 2
- STA 534: Design of Experiments
- STA 567: Bayesian Statistics
Applied Numerical Mathematics
- MTH 537: Introduction to Numerical Analysis 1
- MTH 538: Introduction to Numerical Analysis 2
- MTH 539: Method of Applied Math 1
- MTH 540: Method of Applied Math 2
- MTH 550: Network Theory
- MTH 555: Introduction to Complex Systems
- MGF 636: Complex Fin Instruments
- MAE 702 Seminar: Applied Functional Analysis
High Performance and Data Intensive Computing
- MTH 548: Data-Oriented Computing for Mathematicians
- CSE 570: Introduction to Parallel and Distributed Processing
- CSE 587: Data Intensive Computing
- CDA 609: High Performance Computing 1
- CDA 610: High Performance Computing 2
Have questions or want to learn more?
For degree-specific questions, please contact the graduate coordinator at cdsedept@buffalo.edu.
For admissions-related questions, please contact gradeng@buffalo.edu.