Contact Information
Department of Mathematics
377 Altgeld Hall, MC-382
1409 W. Green Street, Urbana, IL 61801
Professor
Department of Industrial and Enterprise Systems Engineering
104C Transportation Building
Research Areas
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.
Research Interests
Applied Mathematics, financial engineering, big data, traffic, precision agriculture, Internet of Things
Education
Ph.D. Maryland, 1991
Additional Campus Affiliations
Professor, Industrial and Enterprise Systems Engineering
Professor, Mathematics
Director of Graduate Admissions, Mathematics
Professor, Statistics
Professor, Biomedical and Translational Sciences
External Links
Recent Publications
Cho, Y., & Sowers, R. (2026). Koopman representations with irregular time intervals. Physica D: Nonlinear Phenomena, 486, Article 135062. https://doi.org/10.1016/j.physd.2025.135062
Gupta, A., Khaliji, M., Gao, R., Qiu, J., Sowers, R., Chiu, C. Y., & Hernandez, M. E. (2026). ARTIFICIAL INTELLIGENCE ENERGY-REGULATION MODELING SYSTEM FOR FATIGUE PREDICTION IN PEOPLE WITH MULTIPLE SCLEROSIS. In Proceedings of the 2026 Design of Medical Devices Conference, DMD 2026 Article V001T03A004 (Proceedings of the 2026 Design of Medical Devices Conference, DMD 2026). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/DMD2026-1035
Alkurdi, A., He, M., Cerna, J., Clore, J., Sowers, R., Hsiao-Wecksler, E. T., & Hernandez, M. E. (2025). Extending Anxiety Detection from Multimodal Wearables in Controlled Conditions to Real-World Environments. Sensors, 25(4), Article 1241. https://doi.org/10.3390/s25041241
Alkurdi, A., Clore, J., Sowers, R., Hsiao-Wecksler, E. T., & Hernandez, M. E. (2025). Resilience of Machine Learning Models in Anxiety Detection: Assessing the Impact of Gaussian Noise on Wearable Sensors. Applied Sciences (Switzerland), 15(1), Article 88. https://doi.org/10.3390/app15010088
Dogan, A., Sowers, R. B., & Hernandez, M. E. (Accepted/In press). Comparative Analysis of Physiological and Speech Signals for State Anxiety Detection in University Students in STEM. IEEE Transactions on Affective Computing. https://doi.org/10.1109/TAFFC.2025.3638274