Shaurya Agarwal is an associate professor in the Department of Civil, Environmental and Construction Engineering, He joined UCF in 2018 as an assistant professor and key member of the Future City Initiative. He is the founding director of the Urban Intelligence and Smart City (URBANITY) Lab and currently serves as the director of Future City Initiative.

Agarwal was previously an assistant professor in the electrical and computer engineering department at California State University, Los Angeles. He completed his post-doctoral research at New York University

His research focuses on interdisciplinary areas of cyber-physical systems, smart and connected transportation, and connected and autonomous vehicles. Passionate about cross-disciplinary research, he integrates control theory, information science, data-driven techniques and mathematical modeling in his work. He has published one book, more than 35 peer-reviewed publications and multiple conference papers. His work has been funded by several private and government agencies including Oculus, the Federal Highway Administration and Florida Department of Transportation. He is a senior member of the Institute of Electrical and Electronics Engineers (IEEE) and serves as an associate editor of IEEE Transactions on Intelligent Transportation Systems.

• Huang, AJ., Biswas, A., Agarwal, S. (2024). Incorporating Nonlocal Traffic Flow Model in Physics-informed Neural Networks. IEEE Intelligent Transportation Systems Transactions (Impact Factor 7.9).
• Huang, AJ*., Agarwal, S. (2023). On the Limitations of Physics-informed Deep Learning: Illustrations Using First Order Hyperbolic Conservation Law-based Traffic Flow Models. IEEE Open Journal of Intelligent Transportation Systems, 4, 279-293 (Impact Factor 4.6).
• Das, S., Mustavee, S., Agarwal, S., Hasan, S. (2023). Koopman-theoretic Modeling of Quasiperiodically Driven Systems: Example of Signalized Traffic Corridor. IEEE Transactions on Systems, Man and Cybernetics: Systems, 53(7), 4466-4476. (Impact Factor 8.6)
• Huang, AJ., Agarwal, S. (2022). Physics-informed deep learning for traffic state estimation: illustrations with LWR and CTM Models. IEEE Open Journal of Intelligent Transportation Systems 3, 503-518 (Impact Factor 4.6)
• Mustavee, S., Agarwal, S., Enyioha, C., & Das, S. (2022). A linear dynamical perspective on epidemiology: interplay between early COVID-19 outbreak and human mobility. Nonlinear Dynamics, 109(2), 1233-1252.
• Muhlmeyer, M., Agarwal, S., & Huang, J.* (2020). Modeling Social Contagion and Information Diffusion in Complex Socio-Technical Systems. IEEE Systems Journal, vol. 14, no. 4, pp. 5187-5198.
• Kachroo, P., Saiewitz, A., Raschke, R., Agarwal, S., & Huang, J. (2020). A New Language and Input-Output Hidden Markov Model for Automated Audit Inquiry. IEEE Intelligent Systems, vol. 35, no. 6, pp. 39-49.