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 Science and Engineering (CDSE) Ph.D. program is an interdisciplinary Ph.D. 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 CDSE Ph.D. 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.
417 Cooke Hall
We accept applications on a rolling basis throughout the year, but encourage all prospective students to submit their applications by the deadlines noted below.
Apply by February 1
Detailed information on what to include in your online application packet is outlined below.
A nonrefundable fee of $85 is required to apply. You may pay the application fee online with a credit card or e-check.
Copies of transcript(s) for all post-secondary schoolwork must be uploaded with the online application for initial review. Upon an offer of admission, accepted applicants will be required to submit official transcripts and proof of degree(s).
Three letters of recommendation are required to apply to this program. Letters are to be requested through the online application by providing the names and email addresses of recommenders. While we will accept letters from professional sources, we strongly prefer letters from professors who are acquainted with your academic interests, achievements, and abilities.
As part of the application, the IAD requires a statement of purpose briefly describing your background and your academic and career goals.
The GRE is optional for this program. If you would like to take the GRE, arrangements to take the exam can be made through the Educational Testing Service.
Note: the University at Buffalo Institutional Code is 2925.
International applicants are required to provide proof of English proficiency via the Test of English as a Foreign Language (TOEFL) score, International English Language Testing System (IELTS) score, or Pearson Test of English Academic (PTE Academic). All applicants whose native language is not English will be required to provide proof of English proficiency. Please use Institution Code 2925 to provide your scores to us.
The University at Buffalo has the following minimum admission requirements for these tests:
The exam results must be dated within 2 years from your proposed date of admission and remain valid upon entering the term for which you applied. For example, Fall 2020 begins in August 2020; therefore, your exam results must be valid until August 2020.
Information and arrangements to take the exam can be made by contacting the Educational Testing Service. 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 our application deadline.
Fill out the International Applicant Financial Form. You will need to fill out the form labeled "Standard Graduate" for the appropriate academic year. The Financial Form and supporting bank documents may be uploaded to your application after an admissions decision has been made.
All international applicants must submit a completed financial statement. Answer all questions thoroughly. An I-20 cannot be issued without this statement documenting the necessary funds for each year of intended study (five years for a PhD program).
Please note that original financial documents must be brought to the school in person upon arrival at UB orientation.
Please do not mail application materials. All items should be submitted electronically with your online application. Please login to the Application Management System frequently to ensure that all of their supporting documents have been received.
The CDSE PhD program requires a minimum of 72 credit hours. Additional requirements include passing qualifying exams and preparing your dissertation.
When you first start our program, you will select an advisor and 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 and data sciences field. 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:
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.
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
MAE 701 Special Topics: Bayesian Methods in Engineering Applications
CSE 704 Seminar: Big Data
CSE 740 Seminar: Big Data/Machine Learning
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
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