People

Principle Investigator (PI)

Dr. Ameya D. Jagtap

Assistant Professor (Tenure Track),

Aerospace Engineering Department,

Worcester Polytechnic Institute (WPI), USA.

Email: ajagtap@wpi.edu

Education: 

• Ph.D. (Doctor of Philosophy), Aerospace Engineering, Indian Institute of Science, India (2016)

• M.E. (Master of Engineering),  Aerospace Engineering, Indian Institute of Science, India (2010)

Professional Positions:

• Assistant Professor (Tenure-Track), Aerospace Engineering, Worcester Polytechnic Institute, USA  (2024 - Present)

• Assistant Professor of Applied Mathematics (Research), Division of Applied Mathematics, Brown University, USA (2021 - 2024)

• Postdoctoral Research Associate, Division of Applied Mathematics, Brown University, USA (2019 - 2020)

• Postdoctoral Fellow, Tata Institute of Fundamental Science - Center for Applicable Mathematics, India (2016 - 2018)

Research Interest: Dr. Jagtap specializes in the advancement of scientific machine learning, developing robust algorithms that seamlessly integrate high-fidelity data with fundamental physical principles to solve complex problems in computational physics. His expertise spans a broad spectrum of data- and physics-driven methods, including deep learning, uncertainty quantification, and multi-scale/multi-physics simulations across fluid mechanics, solid mechanics, and acoustics. In addition to his work in reinforcement learning-based control, he is deeply well-versed in classical numerical frameworks such as spectral and finite element methods, WENO/DG schemes, and domain decomposition techniques. Dr. Jagtap continues to push the boundaries of the field by incorporating innovative architectures; including deep generative models, graph neural networks, and quantum neural networks, while exploring cutting-edge frontiers in spiking neural networks and bio-inspired computing to bridge the gap between traditional numerical analysis and modern artificial intelligence.

Editorial Board Member: He is serving on the editorial board of the following international journals.

• Neural Networks, Elsevier (IF: 7.8) 

• Neurocomputing, Elsevier (IF: 6.0) 

• Journal of Machine Learning Research (IF: 5.177)

• Frontiers in Neuroinformatics (IF:  3.5)

• Frontiers in Computational Neuroscience (IF:  3.2) 

Moreover, he has reviewed more than 600 manuscripts for over 100 international journals and has served as a proposal reviewer on panels for several prestigious funding agencies, including the National Science Foundation, USA (CAREER Awards and Fluid Dynamics Program),  U.S. Department of Energy, the Swiss National Science Foundation, Switzerland, and Agency for Science, Technology and Research (A*STAR), Singapore, and Natural Sciences and Engineering Research Council of Canada (NSERC).

Ph.D. Student

Sidharth S. Menon

Aerospace Engineering Department, WPI

Sidharth has a strong interest in research at the intersection of scientific computing, engineering, and machine learning, particularly in solving complex problems. His background includes experience gained in both academic research and industry, with projects spanning materials, fluids, and dynamical systems. Before joining WPI for his Ph.D., Sidharth completed a research-focused Master’s degree at the University of Washington, Seattle.

Ph.D. commenced in August 2024

Ph.D. Student 

Trishit Mondal

Aerospace Engineering Department, WPI

Trishit's research interests include scientific machine learning, data-driven modeling of dynamical systems, and high-performance computing. His past projects have focused on modeling landslides using particle-based hydrodynamicsand solving inverse problems in functional spaces related to solid and fluid mechanics. He also has experience in robotics development, particularly in path planning and navigation. Before joining WPI for his Ph.D., Trishit completed Integrated M.Tech (Earth Science) degree from IIT Roorkee.

Ph.D. commenced in August 2025

M.S. Student 

Shinto Mathew

Aerospace Engineering Department, WPI

Mathew’s interests include structural and thermal analysis, as well as finite element analysis. His work focuses on machine-learning-based material identification and characterization.