PNNL

Soumya is a data scientist in the Auto Learning and Reasoning team, Physical & Computational Science Directorate. She was previously a post-doc research associate in the Analytics and Learning team within the Optimization and Control group. Her work draws from various disciplines including optimal control theory, deep learning, reinforcement learning, and probabilistic programming for improved system design, modeling, and control. Her current interests lie in physics-informed formulations that bridge the gap between traditional deep learning methods and classical system and control analysis paradigms to realize more powerful, reliable, robust control-oriented system representations. She received her PhD in aeronautics and astronautics from the University of Washington in 2019. Her work centered on building data-driven algorithms for navigation, estimation, and tracking for autonomous vehicles. At Pacific Northwest National Laboratory, she has been involved in modeling, optimization, and uncertainty quantification for building energy systems, physics-informed analysis of generic deep learning models, and parking prediction models for dynamic curb allocation.

• PhD, Aeronautics and Astronautics – Robotics and Data-driven Control, University of Washington, Seattle, WA (2019)
• MS, Aeronautics and Astronautics – Dynamics and Control, University of Washington, Seattle, WA (2013)
• BEngg, Information Science and Engineering, P.E.S. Institute of Technology, Bangalore, India (2007)

• IEEE Control Systems Society, Women in Engineering
• Organizer: 
  • AMLIES Workshop at ACM e-Energy Conference (2021)
  • Internal PNNL Physics-informed Machine Learning Workshop (2021)
• Reviewer:
  • IEEE Transactions on Aerospace and Electronic Systems, American Control Conference, Learning for Dynamics and Control, Climate Change AI grants

• Varanasi-endowed Fellowship (2013)

2024

• Ramachandran T., A. Rahman, S.S. Vasisht, and R. Hossain. 2024. Scalable Control Co-design for Resilient-by-Design Cyber Physical Systems. PNNL-37048. Richland, WA: Pacific Northwest National Laboratory. Scalable Control Co-design for Resilient-by-Design Cyber Physical Systems
• Ramachandran T., S.S. Vasisht, A. Rahman, A. Bhattacharya, and V.A. Adetola. 2024. "A Computational Framework for Control Co-design of Resilient Cyber-Physical Systems with Applications to Microgrids." IEEE Transactions on Control Systems Technology 32, no. 3:793 - 804. PNNL-SA-183583. doi:10.1109/TCST.2023.3342144

2023

• Bakker C., S.S. Vasisht, S. Huang, and D.L. Vrabie. 2023. "Sensor and Actuator Attacks on Hierarchical Control Systems with Domain-Aware Operator Theory." In Resilience Week (RWS 2023), November 27-30, 2023, National Harbor, MD, 1-8. Piscataway, New Jersey:IEEE. PNNL-SA-184521. doi:10.1109/RWS58133.2023.10284668
• Shaw Cortez W.E., S.S. Vasisht, A.R. Tuor, J. Drgona, and D.L. Vrabie. 2023. "Domain-aware Control-oriented Neural Models for Autonomous Underwater Vehicles." In 12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022), January 4-6, 2023, Canberra, Australia. IFAC-PapersOnline, edited by J. Trumpf and R. Mahony, 56, 228-233. Amsterdam:Elsevier. PNNL-SA-175459. doi:10.1016/j.ifacol.2023.02.039

2022

• Bakker C., A. August, S. Huang, S.S. Vasisht, and D.L. Vrabie. 2022. "Deception-Based Cyber Attacks on Hierarchical Control Systems using Domain-Aware Koopman Learning." In Resilience Week (RWS 2022), September 26-29, 2022, National Harbor, MD, 1-8. Piscataway, New Jersey:IEEE. PNNL-SA-173605. doi:10.1109/RWS55399.2022.9984030
• Drgona J., A.R. Tuor, S.S. Vasisht, and D.L. Vrabie. 2022. "Dissipative Deep Neural Dynamical Systems." IEEE Open Journal of Control Systems 1. PNNL-SA-174718. doi:10.1109/OJCSYS.2022.3186838

2021

• Bhattacharya A., S.S. Vasisht, V.A. Adetola, S. Huang, H. Sharma, and D.L. Vrabie. 2021. "Control Co-Design of Commercial Building Chiller Plant using Bayesian Optimization." Energy and Buildings 246. PNNL-SA-158439. doi:10.1016/j.enbuild.2021.111077
• Drgona J., A.R. Tuor, S.E. Skomski, S.S. Vasisht, and D.L. Vrabie. 2021. "Deep Learning Explicit Differentiable Predictive Control Laws for Buildings." In 7th IFAC Conference on Nonlinear Model Predictive Control, (NMPC 2021), July 11-14, 2021 Bratislava. IFAC-PapersOnLine, edited by G. Pannocchia, et al, 54, 4-19. PNNL-SA-159943. doi:10.1016/j.ifacol.2021.08.518
• Skomski E., S.S. Vasisht, C.L. Wight, A.R. Tuor, J. Drgona, and D.L. Vrabie. 2021. "Constrained Block Nonlinear Neural Dynamical Models." In Proceedings of the American Control Conference (ACC 2021), May 25-28, 2021, Virtual, New Orleans, LA, 2021, 3993 - 4000. Piscataway, New Jersey:IEEE. PNNL-SA-156893. doi:10.23919/ACC50511.2021.9482930