Computational and Data Enabled Sciences PhD

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 - Intro 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 517LEC - Categorical Data Analysis Credits: 3
STA 522LEC - Intro Theoretical Stat 2 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 731LEC - Functional Analysis Credits: 3

High Performance and Data Intensive Computing

CDA 601TUT - Individual Problems Credits: 1 - 3
IE 551LEC - Simul & Stochastic Models 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

Total Credit Hours: 72

3.0 Degree GPA required

Program Learning Outcomes (PLOs)

1) Ability to conduct research and professional practice

2) Demonstrate broad knowledge in core areas in Computational and Data-enabled Science and Engineering

3) Demonstrate advancement of knowledge in a focused area in Computational and Data-enabled Science and Engineering

4) Demonstrate ability to communicate knowledge effectively

SED Statement

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