Seeding high quality interdisciplinary research.
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.
Our curriculum is designed around three core subjects:
A minimum of 9 credit hours from the approved list of core courses must be taken. 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 primary advisor, dissertation committee, and the Director of Graduate Studies. We strongly suggest you finish this course work 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 563: 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
CDSE Students can register for CDA 601 Individual Problems with their major advisor. Individual problems can be used for independent study courses or opportunities for applied work in industry (internships). Students will need to submit a 1-page description of the project they are working on and have it signed by both the major advisor and the Director of Graduate Studies. For students completing a project in industry via internship, the student must submit an offer letter and description of how the work is related to their CDSE studies. Students will send written updates to their major advisor.
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:
Students must pass an oral examination on the needed background material to succeed in the field. Your dissertation committee will provide a written list of topics and courses. The exam may be retaken once within a 12-month period. This requirement should be met by the end of the third semester of study. Once the technical report and the oral examination have successfully been completed, you may file an application for PhD candidacy.
Students must take courses to support their dissertation research. These courses, totaling 12 credits, must be completed by the end of your fifth semester. Depending on your dissertation topic, you may be advised to take elective courses that inform your research topic, or you may be advised to take other courses to broaden your CDSE-related knowledge.
The dissertation must be original and contribute to the furthering of the field.