Hi~ I am currently a Postdoctoral Associate affiliated with MIT Energy Initiative at Massachusetts Institute of Technology. I am also a research scientist at Singularity Energy, where I lead the development of carbon flow analysis and decarbonization solution products. I received my PhD degree from Harvard University in 2022. I received my double B.S. degrees in engineering physics and economics from Tsinghua University in 2015, and my master’s degree in electrical engineering from Tsinghua University in 2017.
My research interests lie in the intersection of control, learning, and optimization for human-cyber-physical systems, with particular applications to smart grids and smart cities. I am passionate about developing theoretical foundations and practically appliable tools that lead to intelligent, autonomous, and sustainable energy systems. In addition, I am broadly interested in data-driven decision-making, distributed optimization and control, online learning and human-in-the-loop control, model-free optimization and control, reinforcement learning, (distributionally) robust optimization, etc.
I will chair a session “Advanced Learning and Optimization for Carbon-Neutral Electricity” in 2023 INFORMS Annual Meeting, Oct. 15-18, Phoenix, Arizona, USA. Welcome to drop by.
[04/2023] I won the Best Research Award (one of two, out of over 100 participants) with the Grid Edge Grand Prize of $5,000 in the Ph.D. Dissertation Challenge in IEEE PES Grid Edge Technologies Conference and Exposition 2023.
[03/2023] My Ph.D. thesis was selected as one of the four Outstanding Doctoral Dissertations (2020-2022) by IEEE Power & Energy Society (PES). I am also invited to present my thesis work in the PES outstanding doctoral dissertation panel in the 2023 IEEE PES General Meeting.
[02/2023] Check our new preprint “Continuous-Time Zeroth-Order Dynamics with Projection Maps: Model-Free Feedback Optimization with Safety Guarantees”, which introduces a class of model-free feedback methods (termed “Projected Zeroth-Order (P-ZO) dynamics”) for solving generic constrained optimization problems with projection. This is the “Part-I” paper that focuses on the problem formulation and theoretical analysis, and our “Part-II” paper on the practical applications of P-ZO methods will appear soon.
[02/2023] I was selected as a finalist for the Ph.D. Dissertation Challenge at the IEEE PES Grid Edge Technologies Conference and Expo. Here is the banner.
[06/26/2022] Check our new preprint “Model-Free Feedback Constrained Optimization Via Projected Primal-Dual Zeroth-Order Dynamics”, which proposes a continuous-time zeroth-order dynamics algorithm that can autonomously steer a black-box system to the optimal solution of a constrained optimization problem using only output feedback.
[05/15/2022] Our paper “Improve Single-Point Zeroth-Order Optimization Using High-Pass and Low-Pass Filters” was accepted for the 39th International Conference on Machine Learning (ICML 2022).
[02/18/2022] Our review paper “Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges” was accepted for publication in IEEE Transactions on Smart Grid.
[01/2022] I passed my PhD dissertation defense!!!!
[12/2021] I received the Outstanding Student Paper Award in 2021 IEEE Conference on Decision and Control (CDC) for our paper “Safe Model-Free Optimal Voltage Control via Continuous-Time Zeroth-Order Methods”, which is also a finalist for the Best Student Paper Award.
[11/02/2021] Check our new preprint “Improve Single-Point Zeroth-Order Optimization Using High-Pass and Low-Pass Filters”. This is an interesting work that borrows the idea of high-pass and low-pass filters from extremum seeking control to design new single-point zeroth-order optimization algorithms with significant performance improvement.
I co-chaired a session “data-driven optimization and control for power systems” in 2021 INFORMS Annual Meeting, Oct. 24-27, Anaheim, California, USA.
[07/27/2021] Our paper “Model-Free Optimal Voltage Control via Continuous-Time Zeroth-Order Methods” was accepted for publication in 2021 60th IEEE Conference on Decision and Control (CDC).
[06/09/2021] Our paper “Online Learning and Distributed Control for Residential Demand Response” was accepted for publication in IEEE Transactions on Smart Grid.
[03/25/2021] Our new preprint on model-free control “Model-Free Optimal Voltage Control via Continuous-Time Zeroth-Order Methods” is available on Arxiv.
[03/18/2021] Our paper “Leveraging Two-Stage Adaptive Robust Optimization for Power Flexibility Aggregation” was accepted for publication in IEEE Transactions on Smart Grid.
[01/26/2021] Check our review paper on applying RL to power systems: “Reinforcement Learning for Decision-Making and Control in Power Systems: Tutorial, Review, and Vision”.
[10/11/2020] Check out our new preprint on demand response “Online Learning and Distributed Control for Residential Demand Response”.
[08/31/2020] Our paper “Distributed Automatic Load Frequency Control with Optimality in Power Systems” was accepted for publication in IEEE Transactions on Control of Network Systems.
[07/15/2020] Our paper “Online Residential Demand Response via Contextual Multi-Armed Bandits” was accepted for publication in 2020 59th IEEE Conference on Decision and Control (CDC).
[07/01/2020] Our work “Exponential Stability of Primal-Dual Gradient Dynamics with Non-Strong Convexity” was presented in 2020 American Control Conference (ACC).
[06/06/2020] Our paper “Online Residential Demand Response via Contextual Multi-Armed Bandits” was accepted for publication in The IEEE Control Systems Letters.
[06/03/2020] This new personal website said “HelloWorld”.