2024-2025 Graduate Catalog
Data Science and Engineering Program
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Graduate Program Director: Catalin Georgescu, Ph.D.
Department of Computer Science
Arts & Sciences, Room 201
414 East Clark Street
Vermillion, SD 57069
Phone: 605-658-6840
cs@usd.edu
www.usd.edu/computer-science
Department of Mathematical Sciences
Patterson Hall 132E
414 East Clark Street
Vermillion, SD 57069
Phone: 605-658-5970
math@usd.edu
www.usd.edu/math
FACULTY
Professors:
Nan Jiang, Ph.D., Kansas State University. Specialization: Numerical Analysis.
Yuhlong Lio, Ph.D., University of South Carolina. Specialization: Stochastic Processes, Non-parametric Function Estimation, Reliability, Iterative Processes, Survival Analysis.
Gabriel Picioroaga, Ph.D., University of Iowa. Specialization: Operator Algebras and Wavelets.
Dan Van Peursem, Ph.D., University of Nebraska-Lincoln. Specialization: Applied Mathematics.
KC Santosh, Ph.D., INRIA - Universite de Lorraine, France. Specialization: Artificial Intelligence, Pattern Recognition, Image Processing, Computer Vision, Machine Learning, Data Mining, Information Retrieval, Big Data.
Associate Professors:
Farhad Akhbardeh, Ph.D., Rochester Institute of Technology. Specialization: National language processing and machine learning.
Douglas Goodman, Ph.D., University of Minnesota-Twin Cities. Specialization: Algorithms, Simulation, Real-time Systems, Graphics.
Catalin Georgescu, Ph.D., State University of New York-Buffalo. Specialization: Dynamical Systems.
Ramiro H. Lafuente Rodriguez, Ph.D., Bowling Green State University. Specialization: Ordered Algebraic Structures, Lattice Theory, Topology, and Math Education.
Assistant Professors:
Rodrigo Rizk, Ph.D., University of Louisiana at Lafayette. Specialization: Artificial Intelligence, Software and Hardware co-design, and High Performance Computing.
Longwei Wang, Ph.D., Auburn University (Computer Science) and University of Texas at Arlington (Electrical Engineering). Specialization: Generalized AI, Machine Learning, Statistical Pattern Recognition, and Data Science.
DEGREE
Program Description
The purpose of the Collaborative Data Science and Engineering PhD Program is to provide for the common delivery of graduate data science programs (coursework, research, and mentorship) by the South Dakota School of Mines & Technology (Mines) and the University of South Dakota (USD). This collaborative program builds upon other existing collaborative programs and shared courses between the participating institutions. Through this collaboration, Mines will contribute content, research training, and faculty with expertise in computer science, math, and multiple fields of engineering. USD will contribute content, research training, and faculty with expertise in computer science, artificial intelligence, math and statistics, medicine and health science, business, and biomedical engineering.
ADMISSION REQUIREMENTS
- Completed Graduate Application form found at: https://www.usd.edu/graduate-school/apply-now and a non-refundable application fee of $35.
- Official transcript(s) verifying receipt of an undergraduate degree and previous graduate credit (in English or with translation). Transcripts must be complete (e.g., if currently enrolled, work-in-progress coursework must be included, foreign transcripts must include a grading scale, and for countries that issue, copy of degree certificate/diploma i.e., India, Nepal, etc.). The USD Graduate School and/or academic units retain the right to require credential evaluations from organizations, such as Educational Credential Evaluators/World Education Services (ECE/WES), for a student if such an evaluation is deemed necessary.
- Baccalaureate degree in Mathematics, Computer Science or closely related field from an institution with institutional accreditation for that degree. A minimum undergraduate cumulative GPA of 3.0 on conferred degree and/or graduate cumulative GPA of 3.0 or better, based on a 4.0 scale, on all graduate coursework is required for full admission. Each graduate program may admit students on provisional status per university policy.
- Applicants with degrees from countries other than the United States who have obtained a high school diploma, undergraduate, or graduate degree from an institutionally accredited American college or university or from an accredited institution in the approved list of English-speaking countries are not required to submit an approved English proficiency exam score. For all other applicants with degrees from other countries, a minimum score of 79 on the Internet-Based TOEFL (iBT), 550 on the Paper-Based TOEFL (PBT), 8.5 on the TOEFL Essentials, 6.0 on the IELTS Academic, 53 on the PTE, or 110 on Duolingo is required for graduate admission.
- Applicants are required to submit a 1-page statement of purpose. Please indicate your career aspirations, how will you use the Ph.D., and how you are prepared for the program.
Additional Program Admission Requirements:
- Two (2) professional letters of recommendation are required. One letter must be from an academic recommender who can attest to the applicant’s scholarly abilities.
- Curriculum Vitae (CV), summarizing your educational background and research and professional experiences.
Subject to faculty approval, those who do not meet all of the criteria above may be admitted on a provisional basis.
Application Deadlines
Student Learning Outcomes for Data Science and Engineering (Ph.D.)
- Acquire and apply the knowledge and skills to make an original contribution to the field of Data Science and Engineering.
- Conduct independent research within a supportive multidisciplinary framework.
- Understand and critically evaluate the relevant literature in Data Science and Data Engineering.
- Communicate relevant Data Science and Engineering principles and theories by written, oral, and visual means.
- Apply Data Science and Engineering principles and procedures to the recognition, interpretation, and understanding of prior and current knowledge in the field.
- Exhibit and appropriate awareness of and commitment to the ethical conduct of research.
Programs
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