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Energy and Environment Directorate

Sayak Mukherjee

Sayak Mukherjee

(509) 375-1477


Sayak Mukherjee is a Staff Scientist in the Optimization and Control group at the Pacific Northwest National Laboratory. He received Ph.D. in Electrical Engineering from North Carolina State University, USA in 2020 and B.E.E. from Jadavpur University, India in 2015. He joined PNNL as a Post-doctoral Research Associate. He is currently working on several problems on system-theoretic approaches for reinforcement learning (RL) based control of dynamical systems with applications to power and energy systems. His areas of expertise include system theory, control, learning especially data-driven optimal control using reinforcement learning and adaptive dynamic programming, AI/ML for dynamics, resilient control designs, large-scale power system stability and control, grid operation with distributed energy resources, etc.

Research Interests

  • Learning for Systems and Control with Guarantees
  • Optimal and Robust Control
  • Reinforcement Learning / Adaptive Dynamic Programming
  • Mathematics of AI/ML with focus on Dynamics
  • Power System Dynamics, Stability and Wide-area Control
  • Resilient Control Methods for Cyber-Physical Systems
  • Microgrids and Distributed Energy Resources

Education and Credentials

  • Ph.D., in Electrical Engineering, North Carolina State University, USA, 2020. Dissertation: S. Mukherjee, Ph.D. Dissertation, Data-Driven Reinforcement Learning Control using Model Reduction Techniques: Theory and Applications to Power Systems, 2020, available via NC State Libraries,
  • B.E., in Electrical Engineering, Jadavpur University, India, 2015.

Affiliations and Professional Service

  • IEEE (student member 2015, member 2020)

Awards and Recognitions

  • Outstanding Performance Award, Energy and Environment Directorate, Pacific Northwest National Laboratory, 2020.
  • Medal for second highest percentage (with highest GPA) in B.E., Jadavpur University, India, 2015.

PNNL Publications


  • Kwon K., S. Mukherjee, H. Zhu, and T. Vu. 2023. "Reinforcement Learning-based Output Structured Feedback for Distributed Multi-Area Power System Frequency Control." In Proceedings of the American Control Conference (ACC 2023), May 31- June 2, 2023, San Diego, CA, 4483-4488. Piscataway, New Jersey:IEEE. PNNL-SA-177914. doi:10.23919/ACC55779.2023.10156618
  • Mukherjee S., and T. Vu. 2023. "Reinforcement Learning of Structured Stabilizing Control for Linear Systems with Unknown State Matrix." IEEE Transactions on Automatic Control 68, no. 3:1746 - 1752. PNNL-SA-156272. doi:10.1109/TAC.2022.3155384
  • Mukherjee S., R. Hossain, Y. Liu, W. Du, V.A. Adetola, S. Mohiuddin, and Q. Huang, et al. 2023. "Enhancing Cyber Resilience of Networked Microgrids using Vertical Federated Reinforcement Learning." In IEEE Power & Energy Society General Meeting (PESGM 2023), July 16-20, 2023, Orlando, FL, 1-5. Piscataway, New Jersey:IEEE. PNNL-SA-178010. doi:10.1109/PESGM52003.2023.10252480
  • Vu T., S. Mukherjee, and V.A. Adetola. 2023. "Resilient Communication Scheme for Distributed Decision of Interconnecting Networks of Microgrids." In IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT 2023), January 16-19, 2023, Washington, DC, 1-5. Piscataway, New Jersey:IEEE. PNNL-SA-177453. doi:10.1109/ISGT51731.2023.10066341


  • Drgona J., S. Mukherjee, A.R. Tuor, M. Halappanavar, and D.L. Vrabie. 2022. "Learning Stochastic Parametric Differentiable Predictive Control Policies." In 10th IFAC Symposium on Robust Control Design (ROCOND 2022), August 30 - September 2, 2022, Kyoto, Japan. IFAC-PapersOnLine, 55, 121 - 126. Amsterdam:Elsevier. PNNL-SA-170144. doi:10.1016/j.ifacol.2022.09.334
  • Mukherjee S., J. Drgona, A.R. Tuor, M. Halappanavar, and D.L. Vrabie. 2022. "Neural Lyapunov Differentiable Predictive Control." In Proceedings of the 61st IEEE Conference on Decision and Control (CDC 2022), December 6-9, 2022, Cancun, Mexico, 2097-2104. Piscataway, New Jersey:IEEE. PNNL-SA-171659. doi:10.1109/CDC51059.2022.9992386
  • Mukherjee S., S. Nandanoori, S. Guan, K. Agarwal, S. Sinha, S. Kundu, and S. Pal, et al. 2022. "Learning Distributed Geometric Koopman Operator for Sparse Networked Dynamical Systems." In First Learning on Graphs Conference (LoG 2022), December 9-12, 2022, Virtual, Online. Proceedings of Machine Learning Research, 198, 1-17. Maastricht:ML Research Press. PNNL-SA-173554.


  • Drgona J., S. Mukherjee, J. Zhang, F. Liu, and M. Halappanavar. 2021. "On the Stochastic Stability of Deep Markov Models." In Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021), December 6-14, 2021, Virtual, Online. Advances in Neural Information Processing Systems, edited by M. Ranzato, et al, 34. San Jose, California:Curran Associates Inc. PNNL-SA-162811.
  • Mukherjee S., and T. Vu. 2021. "On Distributed Model-Free Reinforcement Learning Control with Stability Guarantee." IEEE Control Systems Letters 5, no. 5:1615-1620. PNNL-SA-155641. doi:10.1109/LCSYS.2020.3041218
  • Mukherjee S., and V.A. Adetola. 2021. "A Secure Learning Control Strategy via Dynamic Camouflaging for Unknown Dynamical Systems under Attacks." In IEEE Conference on Control Technology and Applications (CCTA 2021), August 9-11, 2021, Virtual, Online, 905-910. Piscataway, New Jersey:IEEE. PNNL-SA-159067. doi:10.1109/CCTA48906.2021.9659093
  • Mukherjee S., H. Bai, and A. Chakrabortty. 2021. "Model-based and Model-free Designs for an Extended Continuous-time LQR with Exogenous Inputs." Systems and Control Letters 154. PNNL-SA-156098. doi:10.1016/j.sysconle.2021.104983
  • Vu T., S. Mukherjee, R. Huang, and Q. Huang. 2021. "Barrier Function-based Reinforcement Learning for Emergency Control of Power Systems." In IEEE 60th Conference on Decision and Control (CDC 2021), December 14-17, 2021, Austin, TX, 3652-3657. Piscataway, New Jersey:IEEE. PNNL-SA-160849. doi:10.1109/CDC45484.2021.9683573
  • Vu T., S. Mukherjee, T. Yin, R. Huang, J. Tan, and Q. Huang. 2021. "Safe Reinforcement Learning for Emergency Load Shedding of Power Systems." In IEEE Power & Energy Society General Meeting (PESGM 2021), July 26-29, 2021, Washington DC, 1-5. Piscataway, New Jersey:IEEE. PNNL-SA-157689. doi:10.1109/PESGM46819.2021.9638007

Energy and Environment

Core Research Areas