PNNL

Himanshu Sharma joined Pacific Northwest National Laboratory (PNNL) in 2020 as a research scientist in the Optimization and Controls group. He possesses in-depth expertise in computational modeling and simulations for complex physical systems, advanced data-driven machine learning methods, and high-performance computing. Sharma holds a PhD in mechanical engineering from Iowa State University, with a research focus on developing a data-driven Perron-Frobenius operator-based framework for sensor placement for monitoring indoor air quality under uncertainty. Prior to joining PNNL, Sharma served as a postdoctoral scholar at the Argonne Leadership Computing Facility, Argonne National Laboratory, where he contributed to the development and deployment of probabilistic neural networks for scientific applications at scale, particularly in understanding and analyzing challenges associated with distributed training of neural networks capable of predicting uncertainties. His research interests encompass physics-informed machine learning, reinforcement learning, uncertainty quantification, computational fluid dynamics, and dynamics and control for complex physical systems, including energy systems and smart building systems.

• Dynamical Systems and Controls
• Physics-Informed Machine Learning
• Uncertainty Quantification
• Optimization
• Computational Fluid Dynamics

• PhD in mechanical engineering, Iowa State University
• MS in technology, Indian Institute of Technology
• BS in mechanical engineering, Institute of Technology

• Outstanding Performance Award, PNNL, 2022
• Outstanding Performance Award, PNNL, 2020
• Best Paper Award, American Control Conference, by Energy Systems Technical Committee, American Society of Mechanical Engineering