Piyush Mehta

Dr. Piyush Mehta is an Associate Professor in the Department of Mechanical, Materials and Aerospace Engineering at West Virginia University's Benjamin M. Statler College of Engineering and Mineral Resources. He also serves as an Adjunct Assistant Professor in the Lane Department of Computer Science and Electrical Engineering. As the director of the Astrodynamics, Space Science and Space Technology (ASSIST) Laboratory, founded in 2018, and the Center for Innovation in Space Exploration and Research (CISER), Dr. Mehta leads groundbreaking research at the intersection of astrodynamics, space weather, data science, and space safety and sustainability.

Dr. Mehta received his B.S. in Aerospace Engineering from the University of Kansas in 2009, followed by his Ph.D. in Aerospace Engineering from the same institution in 2013. His academic journey has positioned him as a leading researcher in space systems engineering and space weather applications.

Dr. Mehta's research focuses on several critical areas in space science and engineering. His primary interests include Space Situational Awareness (SSA), Space Traffic Management (STM), and Space Weather modeling with uncertainty quantification. At the ASSIST Lab, his team develops advanced algorithms for precise orbit determination, collision avoidance, and satellite operations. A significant portion of his work involves probabilistic modeling of thermosphere density and satellite drag coefficients, which has direct applications for improving satellite mission operations and space traffic management. His research also extends to neural network applications for space weather forecasting, solar image compression, and flight dynamics uncertainty quantification.

The trends in Dr. Mehta's recent publications demonstrate a strong emphasis on integrating machine learning techniques with traditional physics-based space weather models. His work frequently addresses the challenge of uncertainty quantification in space environment modeling, particularly for thermosphere density and satellite drag coefficient prediction. There is a clear progression toward more sophisticated probabilistic frameworks that combine physics-informed neural networks with traditional modeling approaches. His research group has made significant contributions to space weather forecasting, solar image processing, and space object tracking, with applications spanning from satellite operations to aviation safety during space weather events.

Dr. Mehta has received numerous prestigious awards recognizing his contributions to aerospace engineering and space weather research:

• Elected to the class of 2025 AIAA Associate Fellows
• 2022-23 Statler College Outstanding Researcher Junior Level Award
• Wayne and Kathy Richards Fellowship (2021)
• NSF CAREER award (2021)

As an advisor, Dr. Mehta mentors a diverse team of graduate students across aerospace and electrical engineering disciplines. His current advisees include multiple Ph.D. candidates working on projects related to space situational awareness, machine learning applications in space weather, and computer vision for solar imagery analysis. Dr. Mehta has successfully secured funding from major agencies including NSF, NASA, NOAA, Department of Navy (DoD), Department of Energy, and IARPA. His research group, the ASSIST Lab, is closely affiliated with the Center for KINETIC Plasma Physics, creating a robust interdisciplinary research environment focused on space safety and sustainability.

The ASSIST Lab, founded by Dr. Mehta in 2018, has developed into a significant research center with multiple sponsored projects. The lab maintains strong connections with government agencies including NASA, NOAA, and the Department of Defense, focusing on real-world applications of space science. Dr. Mehta serves on the NASA Space Weather Council and the Space Weather Advisory Group (SWAG), a Federal Advisory Committee for the White House Space Weather Operations, Research and Mitigation subcommittee, demonstrating the national impact of his work. The lab has developed several publicly available tools, including high-fidelity drag coefficient response surface models that have been approved for public release by LANL.