| Real-time optimal power flow Y Tang, K Dvijotham, S Low IEEE Transactions on Smart Grid 8 (6), 2963-2973, 2017 | 200 | 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, 2022 | 115* | 2022 |
| Feeder reconfiguration in distribution networks based on convex relaxation of OPF Q Peng, Y Tang, SH Low IEEE Transactions on Power Systems 30 (4), 1793-1804, 2014 | 84 | 2014 |
| Distributed zero-order algorithms for nonconvex multiagent optimization Y Tang, J Zhang, N Li IEEE Transactions on Control of Network Systems 8 (1), 269-281, 2020 | 73 | 2020 |
| Distributed reinforcement learning for decentralized linear quadratic control: A derivative-free policy optimization approach Y Li, Y Tang, R Zhang, N Li IEEE Transactions on Automatic Control 67 (12), 6429-6444, 2021 | 69 | 2021 |
| Optimal placement of energy storage in distribution networks Y Tang, SH Low IEEE Transactions on Smart Grid 8 (6), 3094-3103, 2017 | 50 | 2017 |
| Analysis of the Optimization Landscape of Linear Quadratic Gaussian (LQG) Control Y Zheng, Y Tang, N Li arXiv preprint arXiv:2102.04393, 2021 | 37* | 2021 |
| On the Performance Bound of Sparse Estimation With Sensing Matrix Perturbation YG Yujie Tang, Laming Chen IEEE Transactions on Signal Processing 61 (17), 4372-4386, 2013 | 28 | 2013 |
| Running primal-dual gradient method for time-varying nonconvex problems Y Tang, E Dall'Anese, A Bernstein, S Low SIAM Journal on Control and Optimization 60 (4), 1970-1990, 2022 | 25 | 2022 |
| Semi-global exponential stability of augmented primal–dual gradient dynamics for constrained convex optimization Y Tang, G Qu, N Li Systems & Control Letters 144, 104754, 2020 | 17 | 2020 |
| A feedback-based regularized primal-dual gradient method for time-varying nonconvex optimization Y Tang, E Dall'Anese, A Bernstein, SH Low 2018 IEEE Conference on Decision and Control (CDC), 3244-3250, 2018 | 15 | 2018 |
| Distributed algorithm for time-varying optimal power flow Y Tang, S Low 2017 IEEE 56th Annual Conference on Decision and Control (CDC), 3264-3270, 2017 | 14 | 2017 |
| Zeroth-order feedback optimization for cooperative multi-agent systems Y Tang, Z Ren, N Li Automatica 148, 110741, 2022 | 11 | 2022 |
| Time-varying optimization and its application to power system operation Y Tang California Institute of Technology, 2019 | 10 | 2019 |
| Improve single-point zeroth-order optimization using high-pass and low-pass filters X Chen, Y Tang, N Li International Conference on Machine Learning, 3603-3620, 2022 | 8* | 2022 |
| Communication-efficient distributed SGD with compressed sensing Y Tang, V Ramanathan, J Zhang, N Li IEEE Control Systems Letters 6, 2054-2059, 2021 | 3 | 2021 |
| Source seeking by dynamic source location estimation T Zhang, V Qin, Y Tang, N Li 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2021 | 2 | 2021 |
| Escaping saddle points in zeroth-order optimization: two function evaluations suffice Z Ren, Y Tang, N Li arXiv preprint arXiv:2209.13555, 2022 | 1 | 2022 |
| Distributed Information-based Source Seeking T Zhang, V Qin, Y Tang, N Li arXiv preprint arXiv:2209.09421, 2022 | 1 | 2022 |
| A second-order saddle point method for time-varying optimization Y Tang, S Low 2019 IEEE 58th Conference on Decision and Control (CDC), 3928-3935, 2019 | 1 | 2019 |
Na LiGordon McKay Professor of Electrical Engineering and Applied Mathematics, HarvardVerified email at seas.harvard.edu
