| Harnessing smoothness to accelerate distributed optimization G Qu, N Li IEEE Transactions on Control of Network Systems 5 (3), 1245-1260, 2017 | 760 | 2017 |
| Reinforcement learning for selective key applications in power systems: Recent advances and future challenges X Chen, G Qu, Y Tang, S Low, N Li IEEE Transactions on Smart Grid 13 (4), 2935-2958, 2022 | 322* | 2022 |
| Accelerated distributed Nesterov gradient descent G Qu, N Li IEEE Transactions on Automatic Control 65 (6), 2566-2581, 2019 | 294* | 2019 |
| Optimal scheduling of battery charging station serving electric vehicles based on battery swapping X Tan, G Qu, B Sun, N Li, DHK Tsang IEEE Transactions on Smart Grid 10 (2), 1372-1384, 2017 | 171 | 2017 |
| On the exponential stability of primal-dual gradient dynamics G Qu, N Li IEEE Control Systems Letters 3 (1), 43-48, 2018 | 157 | 2018 |
| Optimal distributed feedback voltage control under limited reactive power G Qu, N Li IEEE Transactions on Power Systems 35 (1), 315-331, 2019 | 148 | 2019 |
| Real-time decentralized voltage control in distribution networks N Li, G Qu, M Dahleh 2014 52nd Annual Allerton Conference on Communication, Control, and …, 2014 | 145 | 2014 |
| Scalable reinforcement learning for multiagent networked systems G Qu, A Wierman, N Li Operations Research 70 (6), 3601-3628, 2022 | 134* | 2022 |
| A random forest method for real-time price forecasting in New York electricity market J Mei, D He, R Harley, T Habetler, G Qu 2014 IEEE PES general meeting| conference & exposition, 1-5, 2014 | 133 | 2014 |
| Finite-Time Analysis of Asynchronous Stochastic Approximation and -Learning G Qu, A Wierman Conference on Learning Theory, 3185-3205, 2020 | 131 | 2020 |
| Online optimization with predictions and switching costs: Fast algorithms and the fundamental limit Y Li, G Qu, N Li IEEE Transactions on Automatic Control 66 (10), 4761-4768, 2020 | 128* | 2020 |
| Distributed greedy algorithm for multi-agent task assignment problem with submodular utility functions G Qu, D Brown, N Li Automatica 105, 206-215, 2019 | 87* | 2019 |
| Learning optimal power flow: Worst-case guarantees for neural networks A Venzke, G Qu, S Low, S Chatzivasileiadis 2020 IEEE International Conference on Communications, Control, and Computing …, 2020 | 82 | 2020 |
| Scalable multi-agent reinforcement learning for networked systems with average reward G Qu, Y Lin, A Wierman, N Li Advances in Neural Information Processing Systems 33, 2074-2086, 2020 | 80 | 2020 |
| Distributed optimal voltage control with asynchronous and delayed communication S Magnússon, G Qu, N Li IEEE Transactions on Smart Grid 11 (4), 3469-3482, 2020 | 74 | 2020 |
| Multi-agent reinforcement learning in stochastic networked systems Y Lin, G Qu, L Huang, A Wierman Advances in neural information processing systems 34, 7825-7837, 2021 | 65* | 2021 |
| Stability constrained reinforcement learning for real-time voltage control Y Shi, G Qu, S Low, A Anandkumar, A Wierman 2022 American Control Conference (ACC), 2715-2721, 2022 | 47 | 2022 |
| Perturbation-based regret analysis of predictive control in linear time varying systems Y Lin, Y Hu, G Shi, H Sun, G Qu, A Wierman Advances in Neural Information Processing Systems 34, 5174-5185, 2021 | 41 | 2021 |
| Voltage control using limited communication S Magnússon, G Qu, C Fischione, N Li IEEE Transactions on Control of Network Systems 6 (3), 993-1003, 2019 | 38 | 2019 |
| Robustness and consistency in linear quadratic control with untrusted predictions T Li, R Yang, G Qu, G Shi, C Yu, A Wierman, S Low Proceedings of the ACM on Measurement and Analysis of Computing Systems 6 (1 …, 2022 | 35 | 2022 |
Na LiWinokur Family Professor of Electrical Engineering and Applied Mathematics, HarvardVerified email at seas.harvard.edu
