Sayak Mukherjee
Sayak Mukherjee
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Hi, welcome to my webpage!
I am a staff scientist in the Optimization and Control Group at Pacific Northwest National Laboratory (PNNL), Richland, WA. I did my Ph.D. in the Electrical and Computer Engineering Dept., North Carolina State University at Raleigh, and worked as a Post-Doctorate Research Associate at PNNL. I was a visiting scholar at LIDS, MIT in the summer of 2022 from PNNL. During my Ph.D. I worked as an R&D intern at New York Power Authority.
Updates:
IEEE Power Electronics Magazine Article : Cybersecurity Challenges in Low-Inertia Power-Electronics-Dominated Grids
Talks at NCSU ECE Colloquium series and FREEDM 2025 Symposium on Advanced Controls for Inverter-Dominated Grids
Accepted IEEE TSG article on Resilient Control of Networked Microgrids using Vertical Federated Reinforcement Learning: Designs and Real-Time Test-Bed Validations (link )
Two invited sessions organized at INFORMS 2024 and IECON 2024.
Work on Deep Multi-Agent Reinforcement Learning for Real-World Signalized Traffic Corridor Control accepted in ICMLA
Book chapter on Advanced computational techniques for improving resilience of critical energy infrastructure under cyber-physical attacks, Taylor and Francis
Papers on risk-constrained inverter-dominated grid control at IEEE Control Systems Letters, ACC and CDC: p1 p2 p3
ACC 2023 workshop invited talk on Some Perspectives on the Scalability and Resiliency for Reinforcement Learning Control
New paper on Learning on Graphs (LoG) conference on Graph-based Koopman modeling
CDC papers on Neural Lyapunov DPC and Data-Driven Pole Placement in LMI Regions with Robustness Guarantees
Paper on Stochastic Parametric Differentiable Predictive Control is accepted at IFAC ROCOND (arxiv full version )
New paper on the Structured Stabilizing RL is accepted in IEEE Transactions on Automatic Control (Link )
Papers on Safe RL for Power System Emergency Voltage Control at IEEE CDC and NeurIPS Safe RL workshop (arxiv link )
Accepted at the NeurIPS 21 on Stochastic Stability of Deep Markov Models (Paper )
SIAM Annual Meeting 21 presentation by Jan -- J. Drgona, S. Mukherjee, J. Zhang, M. Halappanavar, F. Liu, ``On Stability of Deep Neural Network-Based Models", 2021.
Paper accepted for Systems and Control Letters (An extended LQR design - model-based and model-free)
New paper at the IEEE Transactions on the Smart Grid on Scalable RL for Oscillation Control (IEEE Xplore link)
Talk at the Duke Energy Research Symposium
Singular Perturbation driven Approximated Learning Control paper is accepted at Automatica
Research Interests
Research Interests
Theoretical & Computational tools: Optimal and Robust Control, Data-driven Optimal Control using Reinforcement Learning, AI/ML, Resilient Secured Controls, Distributed Control, Machine Learning using Graph Neural Nets, Dynamic System interpretation of Neural Nets.
Applications: Energy Systems, Large-scale Power grid Dynamics and Control, Grid resiliency, Grid reliability, Analysis and Control of Wind-integrated Power Systems, Distributed Energy Resources, Grid-forming Inverters and Power Electronics-dominated Grids, Reliable AI/ML Designs for Infrastructure, Transportation Systems.
Education
Education
Aug., 2015 - May, 2020, Ph.D. in Electrical Engineering, North Carolina State University, Raleigh, NC, USA. Dissertation: Data-Driven Reinforcement Learning Control using Model Reduction Techniques: Theory and Applications to Power Systems. FREEDM Systems Center, Electrical and Computer Engineering, NC State, Advisor : Dr. Aranya Chakrabortty, GPA : 4.0/4.0, Major GPA : 4.277.
2011-2015, B.E., Electrical Engineering, Dept. of Electrical Engineering, Jadavpur University, Kolkata, India. First Class Honours. CGPA : 9.38/10 (Highest CGPA in EE), Percentage : 87.31.
Publications
Publications
Book Chapters, Magazine Articles, and Reports:
B3: Abu-Rub, O.H., Zare, A., Zhang, Z.J., Saeedifard, M., Shadmand, M., Mukherjee, S., Hossain, R.R. and Adetola, V., 2025. Cybersecurity Challenges in Low-Inertia Power-Electronics-Dominated Grids. IEEE Power Electronics Magazine, 11(4), pp.20-30.
B2: Vu, T.L., Mukherjee, S., Kwon, K.B. and Adetola, V.A., 2024. Peer-to-peer communication control for resilient operations of networked cyberphysical systems (PNNL report no. PNNL-37009). Pacific Northwest National Laboratory (PNNL), Richland, WA (United States).
B1: Nazir, N., Nandanoori, S.P., Long, T., Mukherjee, S., Kundu, S. and Adetola, V., 2024. Advanced computational techniques for improving resilience of critical energy infrastructure under cyber-physical attacks. In Cyber Physical System 2.0 (pp. 302-331). CRC Press.
Journals:
J12: Security Risks of AI/ML with a Focus on Reinforcement Learning: A Review and Perspectives from Grid Applications, Kwon, K.B., Mukherjee, S., Hossain, R. R., Adetola, V., 2024, Submitted to IEEE Transactions on Emerging Topics in Computational Intelligence.
J11: Mukherjee, S.*, Hossain, R.R.*, Mohiuddin, S.M.*, Liu, Y., Du, W., Adetola, V., Jinsiwale, R.A., Huang, Q., Yin, T. and Singhal, A., 2024. Resilient Control of Networked Microgrids using Vertical Federated Reinforcement Learning: Designs and Real-Time Test-Bed Validations. IEEE Transactions on Smart Grid.
J10. Kwon, K.B.*, Hossain, R.R.*, Mukherjee, S.*, Chatterjee, K., Kundu, S., Nekkalapu, S. and Elizondo, M., 2024. Coherency-aware learning control of inverter-dominated grids: A distributed risk-constrained approach. IEEE Control Systems Letters.
J9. Kwon, K.B., Mukherjee, S., Vu, T.L. and Zhu, H., 2023. Risk-Constrained Reinforcement Learning for Inverter-Dominated Power System Controls. IEEE Control Systems Letters.
J8. S. Mukherjee, T.L. Vu, "Reinforcement Learning of Structured Control for Linear Systems with Unknown State Matrix", accepted in IEEE Transactions on Automatic Control, 2022.
J7. S. Mukherjee, H. Bai, A. Chakrabortty, “Model-based and Model-free Designs for an Extended Continuous-time LQR with Exogenous Inputs”, Systems and Control Letters, Elsevier, 2021.
J6. S. Mukherjee, H. Bai, A. Chakrabortty, “Reduced-Dimensional Reinforcement Learning Control using Singular Perturbation Approximations”, Automatica, 2021.
J5. S. Mukherjee, R. Huang, Q. Huang, T.L. Vu, T. Yin, “Scalable Voltage Control using Structure-Driven Hierarchical Deep Reinforcement Learning”, submitted, arXiv preprint arXiv:2102.00077, 2021.
J4. S. Mukherjee, T.L. Vu, “On Distributed Model-Free Reinforcement Learning Control with Stability Guarantee”, IEEE Control Systems Letters, 2020.
J3. S. Mukherjee, A. Chakrabortty, H. Bai, A. Darvishi, B. Fardanesh, “Scalable Designs for Reinforcement Learning-based Wide-Area Control”, IEEE Transactions on Smart Grid, 2020.
J2. S. Mukherjee, A. Chakrabortty, S. Babaei, “Modeling and Quantifying the Impact of Wind Power Penetration on Slow Coherency of Power Systems”, IEEE Trans. on Power Systems, 2020.
J1. S. Mukherjee, S. Babaei, A. Chakrabortty, B. Fardanesh “Designing a Measurement-driven Optimal Controller for an Utility-Scale Power System: A New York State Grid Perspective”, International Journal of Power and Energy Systems, Elsevier, 2020.
Conferences:
C30. S Shuvo, S Mukherjee, S Chatterjee, S Glavaski, D Vrabie, G Canayon, M Juckes, R Rodulfo. Deep Multi-Agent Reinforcement Learning for Real-World Signalized Traffic Corridor Control, 23rd IEEE International Conference on Machine Learning and Applications (ICMLA), Coral Gables, Miami, FL.
C29. Mukherjee, S., Hossain, R.R., Chatterjee, K., Kundu, S., Kwon, K.B., Nekkalapu, S. and Elizondo, M., 2024. Structural impact of grid-forming inverters on power system coherency. IEEE IECON (Industrial Electronics Society Conference).
C28. Ferdous, S.M., Neff, R., Peng, B., Shuvo, S., Minutoli, M., Mukherjee, S., Kowalski, K., Becchi, M. and Halappanavar, M., 2024. \texttt {Picasso}: Memory-Efficient Graph Coloring Using Palettes With Applications in Quantum Computing. arXiv preprint arXiv:2401.06713.
C27. Kwon, K.B., Mukherjee, S., Zhu, H. and Vu, T.L., 2023, May. Reinforcement Learning-based Output Structured Feedback for Distributed Multi-Area Power System Frequency Control. In 2023 American Control Conference (ACC) (pp. 4483-4488). IEEE.
C26. S. Mukherjee, R. Hossain, Y. Liu, W. Du, V.A. Adetola, S. Mohiuddin, and Q. Huang, et al. "Enhancing Cyber Resilience of Networked Microgrids using Vertical Federated Reinforcement Learning." submitted to IEEE PESGM 2023.
C25. T.L. Vu, S. Mukherjee, V. Adetola, "Resilient Communication Scheme for Distributed Decision of Interconnecting Networks of Microgrids", accepted at IEEE ISGT NA, 2023.
C24. Rahman, A., Bhattacharya, A., Ramachandran, T., Mukherjee, S., Sharma, H., Fujimoto, T. and Chatterjee, S., 2022, November. Adversar: Adversarial search and rescue via multi-agent reinforcement learning. In 2022 IEEE International Symposium on Technologies for Homeland Security (HST) (pp. 1-7). IEEE.
C23. S. Mukherjee, S. Nandanoori, S. Guan, K. Agarwal, S. Sinha, S. Pal, S. Kundu, Y. Wu, D. Vrabie, S. Chowdhury, "Distributed Geometric Koopman Operator Learning for Sparse Networked Dynamical Systems." In the Learning on Graphs (LoG) conference, 2022.
C22. S. Mukherjee, R. Hossain, "Data-Driven Pole Placement in LMI Regions with Robustness Guarantees", IEEE CDC, 2022.
C21. Mukherjee, S., Drgoňa, J., Tuor, A., Halappanavar, M. and Vrabie, D., 2022. Neural Lyapunov Differentiable Predictive Control. IEEE CDC, arXiv preprint arXiv:2205.10728.
C20. Drgoňa, J., Mukherjee, S., Tuor, A., Halappanavar, M. and Vrabie, D., 2022. Learning Stochastic Parametric Differentiable Predictive Control Policies. accepted at IFAC ROCOND, arXiv preprint arXiv:2203.01447.
C19. S. Mukherjee, T.L. Vu, ``Learning the Robust and Structured Control of Unknown Linear Systems", American Control Conference, 2022.
C18. T.L. Vu, S. Mukherjee, R. Huang and Q. Huang, 2021. ``Safe Reinforcement Learning for Grid Voltage Control", Workshop on Safe and Robust Control of Uncertain Systems at the 35th Conference on Neural Information Processing Systems (NeurIPS) 2021.
C17. J. Drgona, S. Mukherjee, J. Zhang, M. Halappanavar, F. Liu, ``On the Stochastic Stability of Deep Markov Models", 35th Conference on Neural Information Processing Systems (NeurIPS) 2021, Sydney, Australia.
C16. J. Zhang, J. Drgona, S. Mukherjee, M. Halappanavar, F. Liu, “Variational Generative Flows for Reconstruction Uncertainty Estimation”, ICML 2021 Workshop on Uncertainty and Robustness in Deep Learning.
C15. T.L. Vu, S. Mukherjee, R. Huang, J. Tan, Q. Huang,``Barrier Function-based Safe Reinforcement Learning for Emergency Control of Power Systems", IEEE Conference on Decision and Control, 2021.
C14. S. Mukherjee, T.L. Vu, ``On Distributed Model-Free Reinforcement Learning Control with Stability Guarantee", American Control Conference (L-CSS presentation), 2021.
C13. T.L. Vu, S. Mukherjee, T. Yin, R. Huang, J. Tan, Q. Huang,``Safe Reinforcement Learning for Emergency Load Shedding of Power Systems", IEEE PES General Meeting, 2021.
C12. S. Mukherjee, V. Adetola, ``A Secure Learning Control Strategy via Dynamic Camouflaging for Unknown Dynamic Systems under Attacks", IEEE CCTA, 2021.
C11. S. Mukherjee, H. Bai, and A. Chakrabortty, ``Reinforcement Learning Control of Power Systems with Unknown Network Model under Ambient and Forced Oscillations", invited paper in IEEE Conference on Control Technology and Applications (CCTA), Montreal, Canada, 2020.
C10. S. Mukherjee, H. Bai, A. Chakrabortty, ``On Robust Reduced-Dimensional Reinforcement Learning Control for Singularly Perturbed Systems' , American Control Conference, Denver, CO, 2020.
C9. S. Mukherjee, H. Bai, A. Chakrabortty, ``Model-free Decentralized Reinforcement Learning Control for Distributed Energy Resources'', IEEE PES General Meeting, 2020.
C8. S. Mukherjee, H. Bai, A. Chakrabortty, ``Block-Decentralized Model Free reinforcement Learning of Two-time Scale Networks", American Control Conference, 2019.
C7. S. Mukherjee, A. Darvishi, A. Chakrabortty, B. Fardanesh, ``Learning Power System Dynamic Signatures using LSTM-Based Deep Neural Network: A Prototype Study on the New York State Grid", IEEE PES General Meeting, Atlanta, GA, 2019.
C6. S. Mukherjee, H. Bai, A. Chakrabortty, ``On Model Free Reinforcement Learning for Singularly Perturbed Systems", IEEE Conference on Decision and Control, Miami, Florida, 2018.
C5. S. Mukherjee, N. Xue, and A. Chakrabortty, ``A Hierarchical Design for Damping Control of Wind-Integrated Power Systems Considering Heterogeneous Wind Farm Dynamics", IEEE Conference on Control Technology and Applications, Denmark, 2018.
C4. S. Mukherjee, S. Babaei, and A. Chakrabortty, ``A Measurement-based Approach for Optimal Damping Control of the New York State Power Grid", IEEE PES General Meeting, Portland, OR, 2018.
C3. Barua, S., Shit, S., Mukherjee, S. and Goswami, S.K., 2015, December. Novel Load Frequency Control with optimum power routing method. In 2015 Annual IEEE India Conference (INDICON) (pp. 1-6). IEEE.
C2. Mukherjee, S., Shit, S., Chakraborty, R. and Chatterjee, D., 2015. Design and analysis of fuzzy tuned proportional resonant controller for shunt active power filter using neutral point clamped multi-level inverter. In Michael Faraday IET International Summit 2015 (p. 71), IET.
C1. Mukherjee, S., Chakraborty, R. and Goswami, S.K., 2015, March. Economic generation scheduling in microgrid with pumped-hydro unit using particle swarm optimization. In 2015 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT) (pp. 1-5). IEEE.
Workshops:
W3. M. Desai, H. Sharma, S. Mukherjee, and S. Glavaski-Radovanovic. 2024. "Uncertainty-Aware Data-Driven Predictive Controller for Hybrid Power Plants." In 4th Annual AAAI Workshop on AI to Accelerate Science and Engineering (AI2ASE).
W2. Vu, T.L., Mukherjee, S., Huang, R. and Huang, Q., 2021. Safe reinforcement learning for grid voltage control. NeurIPS workshop on Safe and Robust Control of Uncertain Systems.
W1. Zhang, J., Drgona, J., Mukherjee, S., Halappanavar, M. and Liu, F., Variational Generative Flows for Reconstruction Uncertainty Estimation. ICML 2021 Workshop on Uncertainty and Robustness in Deep Learning.
Awards and Recognitions
Awards and Recognitions
Outstanding Performance Award, Energy and Environment Directorate, Pacific Northwest National Laboratory. 2024.
Outstanding Performance Award, Energy and Environment Directorate, Pacific Northwest National Laboratory. 2022.
Outstanding Performance Award, Energy and Environment Directorate, Pacific Northwest National Laboratory. 2020.
Medal for second-highest percentage (with highest CGPA) in B.E.E., Jadavpur University, India.
Tenth position in the 10+2 Board Exam, 2011, State of West Bengal, India.
Talks and Visits
Talks and Visits
T17. Invited talk in the ECE Colloquium Series at North Carolina State University, 2025, Distributed Risk-Constrained Learning-Based Controls: Designs Focusing on Inverter-Dominated Grid Operations.
T16. Invited talk at the FREEDM Research Symposium 2025 at North Carolina State University, On the Dynamical Impacts and Advanced Controls for Inverter-dominated Grids.
T15. ACC 2023 invited talk, Some Perspectives on the Scalability and Resiliency for Reinforcement Learning Control.
T14. Sayak Mukherjee, Veronica Adetola, Overview of two program initiatives at PNNL: E-COMP (Energy System Co-Design with Multiple Objectives and Power Electronics) and RD2C (Resilience Through Data-Driven, Intelligently Designed Control), GE Research Symposium 2023.
T13. Sayak Mukherjee, On Improving Resiliency of Networked Microgrids using Federated Reinforcement Learning, invited talk at INFORMS Annual Meeting 2023.
T12. Invited collaboration visit at Laboratory for Information and Decision Systems (LIDS), MIT, 2022 from PNNL.
T11. PES GM Panel talk with Dr. Aranya Chakrabortty, Scalable reinforcement learning-based designs for wide area oscillation control of power systems, 2022.
T10. Pacific Northwest National Laboratory TechFest22 talk, ``Learning Differential predictive Controls with Lyapunov Guarantees", 2022.
T9. SIAM Annual Meeting 21 presentation, J. Drgona, S. Mukherjee, J. Zhang, M. Halappanavar, F. Liu, ``On Stability of Deep Neural Network-Based Models", 2021.
T8. Pacific Northwest National Laboratory TechFest21 talk, ``Making Learning based Controls Scalable by using Limited Structural Information", 2021.
T7. Featured lightning talk at Duke University Energy Data Analytics Symposium on “Scalable Reinforcement Learning-based Control of Distributed Energy Resources”, 2020.
T6. FREEDM, NC State technical tutorial on RL Control for Power Systems, 2020.
T5. Presentation at LIDS, MIT on “Reinforcement Learning Control using Dimensionality Reduction and Applications to Power System Dynamics”, 2020.
T4. Guest lectures on ``Adaptive Optimal Control via Reinforcement Learning" for the NCSU course ECE 792: Adaptive Control and Reinforcement Learning.
T3. Invited talk on ``Reinforcement Learning Based Wide-Area Control of Power Systems Using Dimensionality Reduction Techniques" on behalf of Dr. Aranya Chakrabortty in 2019 Conference on Information Sciences and Systems (CISS), Johns Hopkins University, MD.
T2. North American SynchroPhasor Initiative (NASPI) Meeting, Richmond, VA, 2019. ``Measurement-based Optimal Control of Ultra-Large Power Systems : A Design on the New York State Grid",
T1. Invited student talk on ``Reduced-dimensional Reinforcement Learning Control for Time-scale Separated Dynamical Systems'' in Southeast Controls Conference 2019 at Georgia Tech.
Mentions
Mentions
NCSU ECE Colloquium series talk https://ece.ncsu.edu/seminar/2-28/
IECON 2024 session organization
ACC 2023 workshop invited talk https://goswami78.github.io/ACC_2023_Workshop/mukherjee.html
PNNL at NeurIPS 2021 www.pnnl.gov/events/pnnl-neurips-2021
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