| Harnessing smoothness to accelerate distributed optimization G Qu, N Li IEEE Transactions on Control of Network Systems 5 (3), 1245-1260, 2017 | 407 | 2017 |
| Accelerated distributed Nesterov gradient descent G Qu, N Li IEEE Transactions on Automatic Control 65 (6), 2566-2581, 2019 | 167* | 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 | 105 | 2014 |
| On the exponential stability of primal-dual gradient dynamics G Qu, N Li IEEE Control Systems Letters 3 (1), 43-48, 2018 | 89 | 2018 |
| 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 | 88 | 2017 |
| 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 | 72 | 2014 |
| Optimal distributed feedback voltage control under limited reactive power G Qu, N Li IEEE Transactions on Power Systems 35 (1), 315-331, 2019 | 69 | 2019 |
| 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 | 67* | 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 | 52* | 2019 |
| Finite-Time Analysis of Asynchronous Stochastic Approximation and -Learning G Qu, A Wierman Conference on Learning Theory, 3185-3205, 2020 | 51 | 2020 |
| Scalable reinforcement learning of localized policies for multi-agent networked systems G Qu, A Wierman, N Li Learning for Dynamics and Control, 256-266, 2020 | 38 | 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 | 31* | 2020 |
| 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 | 26 | 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 | 25 | 2020 |
| Reinforcement learning for decision-making and control in power systems: Tutorial, review, and vision X Chen, G Qu, Y Tang, S Low, N Li arXiv preprint arXiv:2102.01168, 2021 | 24 | 2021 |
| Multi-agent reinforcement learning in stochastic networked systems Y Lin, G Qu, L Huang, A Wierman Advances in Neural Information Processing Systems 34, 2021 | 20* | 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 | 20 | 2019 |
| Exploiting fast decaying and locality in multi-agent mdp with tree dependence structure G Qu, N Li 2019 IEEE 58th conference on decision and control (CDC), 6479-6486, 2019 | 16 | 2019 |
| Short-term wind power forecasting based on numerical weather prediction adjustment G Qu, J Mei, D He 2013 11th IEEE International Conference on Industrial Informatics (INDIN …, 2013 | 12 | 2013 |
| Combining model-based and model-free methods for nonlinear control: A provably convergent policy gradient approach G Qu, C Yu, S Low, A Wierman arXiv preprint arXiv:2006.07476, 2020 | 11 | 2020 |
Na LiGordon McKay Professor of Electrical Engineering and Applied Mathematics, HarvardVerifierad e-postadress på seas.harvard.edu
