Computational and Data Enabled Sciences PhD

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The Computational and Data Enabled Sciences PhD addresses pressing research challenges across various fields, such as materials modeling and design, natural disasters, smart electric grids, and medical technologies, by integrating large-scale computing and big data analysis. Through the collaboration of faculty members from departments including Mechanical and Aerospace Engineering, Chemical and Biological Engineering, Computer Science and Engineering, Math, Physics, and Chemistry, the program trains scientists to effectively analyze big data and multisource datasets. By combining expertise in computing, data sciences, and domain sciences, students are equipped with the necessary skills to tackle complex problems and solve grand challenges in their respective fields.

Curriculum

Required Core (27 credits)

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.

Elective Courses (3-15 credits)

Choose at least 3 credits from any sub-discipline:

Data Science

CSE 555LEC - Introduction to Pattern Recognition Credits: 3
CSE 674LEC - Advanced Machine Learning Credits: 3
MAE 700 - Bayesian Methods in Engineering Applications Credits: 3
STA 504LEC - Introduction to Applied Statistics II Credits: 3
STA 515LEC - Distributn Free Inference Credits: 3
STA 517LEC - Categorical Data Analysis Credits: 3
STA 522LEC - Intro Theoretical Stat 2 Credits: 3
STA 551LEC - Stochastic Processes Credits: 3
STA 561LEC - Longitudinal Data Analysis Credits: 3
STA 581LEC - Multivar Data Analysis Credits: 3

Applied Numerical Mathematics

CE 530LEC - Molecular Modeling Credits: 3
IE 576LEC - Applied Stochastic Proc Credits: 3
IE 671LEC - Nonlinear Programming Credits: 3
IE 675LEC - Game Theory Credits: 3
MAE 529LEC - Finite Element Structure Analysis Credits: 3
MAE 540LEC - Computational Fluid Dynamics 2 Credits: 3
MAE 541LEC - Topics Finite Elements Credits: 3
MTH 544LEC - Fundamen Applied Math 2 Credits: 3
MTH 549LEC - Intro Partl Diff Equatns Credits: 3
MTH 551 - Probability Theory Credits: 3
MTH 632LEC - Analysis Credits: 3
MTH 638LEC - Adv Numerical Analysis 2 Credits: 3
MTH 731LEC - Functional Analysis Credits: 3

High Performance and Data Intensive Computing

CDA 601TUT - Individual Problems Credits: 1 - 3
IE 551LEC - Simulation & Stochastic Methods Credits: 3
MAE 610LEC - High Perform Computing 2 Credits: 3
STA 545LEC - Statistical Data Mining I Credits: 3
STA 546LEC - Data Mining II Credits: 3

Culminating Experience (0-12 credits)

MAE 660TUT - Dissertation Credits: 1 - 12
MAE 699TUT - Supervised Research Credits: 1 - 12

Total Credit Hours: 72

3.0 Degree GPA required

Instruction Method

In Person (100 percent of courses offered in person)

Full/Part Time Options

Full Time

Credits Required

72 Hours

Time-To-Degree

4 to 5+ Years

Application Fee

$85

Admissions Tests**

GRE

Program Contact

School of Engineering and Applied Sciences
Office of Graduate Education
415 Bonner Hall
Buffalo, NY 14260
Email: gradeng@buffalo.edu

SED Statement

This program is officially registered with the New York State Education Department (SED).

Online programs/courses may require students to come to campus on occasion. Time-to-degree and number of credit hours may vary based on full/part time status, degree, track and/or certification option chosen. Time-to-degree is based on calendar year(s). Contact the department for details.

** At least one of the admissions tests are required for admission. Test and score requirements/exceptions vary by program. Contact the department for details.