Rate My Professor Piyush Mehta

Piyush Mehta is an associate professor of space systems in the department of mechanical, materials, and aerospace engineering at West Virginia University, where he has served on the faculty since 2018. He directs the Astrodynamics, Space Science, and Space Technology (ASSIST) Laboratory, founded in 2018, and the Center for Innovation in Space Exploration and Research (CISER). Mehta is also an adjunct faculty member in the Lane Department of Computer Science and Electrical Engineering. He earned a Ph.D. in aerospace engineering from the University of Kansas in 2013 and a B.S. in aerospace engineering from the same institution in 2009. His research specializations encompass astrodynamics, space situational awareness, space traffic management, space weather probabilistic modeling and forecasting with uncertainty quantification, space exploration, and flight dynamics and system identification. Mehta develops advanced algorithms for precise orbit determination, collision avoidance, and efficient satellite operations, as well as probabilistic models for forecasting space weather events including solar activity and geomagnetic storms to protect space-based and terrestrial technologies. He further advances spacecraft technologies, propulsion systems, mission planning strategies, and solutions for threat engineering in flight dynamics.

Mehta has earned several major awards and honors, including election to the class of 2025 AIAA Associate Fellows, the 2022-23 Statler College Outstanding Researcher Junior Level Award, the Wayne and Kathy Richards Fellowship in 2021, and the NSF CAREER Award in 2021. He serves on the NASA Space Weather Council and the Space Weather Advisory Group (SWAG), a federal advisory committee for the White House Subcommittee on Space Weather Operations, Research, and Mitigation. Mehta teaches undergraduate and graduate courses such as Spaceflight and Systems, Capstone Spacecraft Design, and Space Weather and Space Systems. His key publications include "Flight Dynamic Uncertainty Quantification Modeling using Physics-Informed Neural Networks" (AIAA Journal, 2024), "Reduced-Order Probabilistic Emulation of Physics-Based Ring Current Models: Application on RAM-SCB Particle Flux" (Space Weather, 2024), "Probabilistic Short-Term Solar Driver Forecasting with Neural Network Ensembles" (Space Weather, 2024), "Satellite drag coefficient modeling for thermosphere science and mission operations" (Advances in Space Research, 2023), and "Stochastic modeling of physical drag coefficient - its impact on orbit prediction and space traffic management" (Advances in Space Research, 2023).