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 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
(716) 645-8774

Admissions Requirements

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:

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.

Curriculum Overview

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.

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