Research

For the updated list of publications, please see my google scholar page.

Journal Papers

Published:

[7] J. Feng, W. Cui, J. Cortes, and Y. Shi, “Bridging Transient and Steady-State Performance in Voltage Control: A Reinforcement Learning Approach with Safe Gradient Flow”, IEEE Control Systems Letters, 2023.

[6] M. Qi, Y. Shi, Y. Qi, C. Ma, R. Yuan, D. Wu, and M. Z. Shen, “A Practical End-to-End Inventory Management Model with Deep Learning,” Management Science, to appear.

[5] Y. Chen, Y. Shi, and B. Zhang, “Data-driven optimal voltage regulation using input convex neural networks,Electric Power Systems Research, vol. 189, 2020.

[4] Y. Shi,  B. Xu, Y. Tan, D. Kirschen, and B. Zhang, “Optimal battery control under cycle aging mechanisms in pay for performance settings,” IEEE Transactions on Automatic Control, vol. 64(6), pp. 2324-2339, 2018.

[3] B. Xu, Y. Shi,  D. Kirschen, and B. Zhang, “Optimal battery participation in frequency regulation markets,” IEEE Transactions on Power Systems, vol. 33(6), pp. 6715-6725, 2018.

[2] Y. Shi, B. Xu, D. Wang, and B. Zhang, “Using battery storage for peak shaving and frequency regulation: Joint optimization for superlinear gains,IEEE Transactions on Power Systems, vol. 33(3), pp. 2882-2894, 2018.

[1] L. Zhou, Y. Shi, J. Wang, and P. Yang, “A balanced heuristic mechanism for multirobot task allocation of intelligent warehouses,” Mathematical Problems in Engineering, 2014.

Under Review & Revisions: 

Conference Papers

[21] L. Bhan, Y. Shi, and M. Krstic, “Operator Learning for Nonlinear Adaptive Control,” Learning for Dynamics & Control Conference (L4DC), 2023.

[20] N. Zheng, X. Liu, B. Xu, and Y. Shi, “Energy Storage Price Arbitrage via Opportunity Value Function Prediction,” IEEE Power & Energy Society General Meeting (PESGM), 2023.

[19] K. Cheng, Y. Chen, and Y. Shi, “GridViz: a Toolkit for Interactive and Multi-Modal Power Grid Data Visualization,” IEEE Power & Energy Society General Meeting (PESGM), 2023.

[18] C. Zhang, Y. Shi, and Y. Chen, “BEAR: Physics-Principled Building Environment for Control and Reinforcement Learning, ACM International Conference on Future Energy Systems (ACM e-Energy), 2023.

[17] Y. Shi, Z. Li, H. Yu, D. Steeves, A. Anandkumar, and M. Krstic, “Machine Learning Accelerated PDE Backstepping Observers,” IEEE Conference on Decision and Control (CDC), 2022.

[16] K. Cheng, Y. Bian, Y. Shi, and Y. Chen, “Carbon-Aware EV Charging,” IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), 2022.

[15] Y. Chen, Y. Shi, D. Arnold, and S. Peisert, “SAVER: Safe Learning-Based Controller for Real-Time Voltage Regulation”, IEEE Power &Energy Society General Meeting (PESGM), 2022.

[14] C. Yeh, J. Yu, Y. Shi, and A. Wierman, “Robust online voltage control with an unknown grid topology,” ACM International Conference on Future Energy Systems (ACM e-Energy), 2022.

[13] Y. Bian, N. Zheng, Y. Zheng, B. Xu, and Y. Shi, “Demand response model identification and behavior forecast with OptNet: a gradient-based approach,” ACM International Conference on Future Energy Systems (ACM e-Energy), 2022.

[12] Y. Shi, G. Qu, S. Low, A. Anandkumar, and A. Wierman, "Stability Constrained Reinforcement Learning for Real-Time Voltage Control," American Control Conference (ACC), 2022.

[11] S. Han, H. Wang, S. Su, Y. Shi, and F. Miao, “Stable and Efficient Shapley Value-Based Reward Reallocation for Multi-Agent Reinforcement Learning of Autonomous Vehicles,” IEEE International Conference on Robotics and Automation (ICRA), 2022.

[10] Y. Huang, H. Zhang, Y. Shi, Z. Kolter, and A. Anandkumar, “Training certifiably robust neural networks with efficient local lipschitz bounds,Advances in Neural Information Processing Systems (NeurIPS), 2021.

[9] G. Qu, Y. Shi, S. Lale, A. Anandkumar, and A. Wierman, “Stable online control of linear time-varying systems,” Learning for Dynamics and Control (L4DC), 2021.

[8] L. Zheng, Y. Shi. L. Ratliff, and B. Zhang, “Safe reinforcement learning of control-affine systems with vertex networks,” Learning for Dynamics and Control (L4DC), 2021.

[7] Y. Shi, and B. Zhang, “Multi-agent reinforcement learning in Cournot games,” IEEE Conference on Decision and Control (CDC), 2020.

[6] D. Mankowitz, N. Levine, R. Jeong, Y. Shi, J. Kay, A. Abdolmaleki, J. Springenberg, T. Mann, T. Hester, M. Riedmiller, “Robust Reinforcement Learning for Continuous Control with Model Misspecification,” International Conference on Learning Representations (ICLR), 2020.

[5] Y. Chen, Y. Shi, and B. Zhang, “Optimal Control Via Neural Networks: A Convex Approach,” In International Conference on Learning Representations (ICLR), 2019.

[4] Y. Shi, B. Xu, Y. Tan, and B. Zhang,A convex cycle-based degradation model for battery energy storage planning and operation,” American Control Conference (ACC), 2018.

[3] B. Xu, Y. Shi, D. Kirschen, and B. Zhang, “Optimal regulation response of batteries under cycle aging mechanisms,” IEEE Conference on Decision and Control, 2017.

[2] Y. Chen, Y. Shi, and B. Zhang, “Modeling and optimization of complex building energy systems with deep neural networks”, Asilomar Conference on Signals, Systems, and Computers, 2017.

[1] Y. Shi, B. Xu, B. Zhang, D. Wang, “Leveraging energy storage to optimize data center electricity cost in emerging power markets, ACM International Conference on Future Energy Systems (ACM e-Energy), 2016.


ML Workshop Presentations:

Preprints