Contact Information
347 Illini Hall
1409 W Green
M/C 382
Urbana, IL 61801
Biography
My interests are in various applied problems which have impact. My formal training is in electrical engineering and applied mathematics. I find that some of the more interesting problems I have worked on have come from looking at the real world with a strongly quantitative toolset. Along the way, I have spent time at a hedge fund and consulted for private industry and the U.S. Government. Finally, at the moment I am the associate head for undergraduate studies in the department of Industrial and Enterprise Systems Engineering.
Research Interests
Applied Mathematics, financial engineering, big data, traffic, precision agriculture, Internet of Things
Additional Campus Affiliations
Professor, Industrial and Enterprise Systems Engineering
Professor, Mathematics
Professor, Center for Digital Agriculture, National Center for Supercomputing Applications (NCSA)
Professor, Biomedical and Translational Sciences
External Links
Recent Publications
Kaur, R., Motl, R. W., Sowers, R., & Hernandez, M. E. (2023). A Vision-Based Framework for Predicting Multiple Sclerosis and Parkinson's Disease Gait Dysfunctions - A Deep Learning Approach. IEEE Journal of Biomedical and Health Informatics, 27(1), 190-201. https://doi.org/10.1109/JBHI.2022.3208077
Kaur, R., Levy, J., Motl, R. W., Sowers, R., & Hernandez, M. E. (Accepted/In press). Deep Learning for Multiple Sclerosis Differentiation Using Multi-Stride Dynamics in Gait. IEEE Transactions on Biomedical Engineering, 1-12. https://doi.org/10.1109/TBME.2023.3238680
Matin, H. N. Z., & Sowers, R. B. (2022). NEAR-COLLISION DYNAMICS IN A NOISY CAR-FOLLOWING MODEL. SIAM Journal on Applied Mathematics, 82(6), 2080-2110. https://doi.org/10.1137/21M1454055
Carmody, D., & Sowers, R. (2021). Topological Analysis of Traffic Pace via Persistent Homology. Journal of Physics: Complexity, 2(2), [025007]. https://doi.org/10.1088/2632-072X/abc96a
Kaur, R., Schaye, C., Thompson, K., Yee, D. C., Zilz, R., Sreenivas, R. S., & Sowers, R. B. (2021). Machine learning and price-based load scheduling for an optimal IoT control in the smart and frugal home. Energy and AI, 3, [100042]. https://doi.org/10.1016/j.egyai.2020.100042