Computational and Data-Enabled Sciences PhD

Students in professor Hachmann's lab looking at data on a computer screen.

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.

About the Program

The Computational and Data-Enabled Science 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
(716) 645-8774

Admissions Requirements

  • Master's degree in related field including but not limited to: engineering, mathematics, business, marketing, pharmacy
  • 3 Letters of recommendation
  • GRE scores
  • GPA of 3.0 or better from your bachelor's degree courses
  • Proof of English language proficiency (for international applicants only)
    • TOEFL (IBT): minimum score 79
    • TOEFL (PBT): minimum score 550
    • IELTS: minimum score 6.5 (with no sub-score below 6.0)
    • PTE Academic: minimum score 55 (with no subsection score below 50)
    • Scores must be less than 2 years old
Application Deadlines

We accept applications on a rolling basis throughout the year, but encourage all prospective students to submit their applications by the deadlines noted below.


Fall enrollment: 
Apply by February 1

Application Materials

Detailed information on what to include in your online application packet is outlined below.

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.

Degree Program Specifics

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 and data sciences field. This technical report must be completed no later than the end of the second semester.

Course Requirements

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

For admissions-related questions, please contact