News

[2024/10] I will present our work Leveraging Predictions in Power System Frequency Control: an Adaptive Approach at 2024 INFORMS Annual Meeting in the session ME75 Advances in Data-driven Controls for Renewable Dominated Grids. Welcome to drop by our talks.

[2024/06] Disertation defense and ECE graduation in one day 🎉. Heartfelt thanks to my advisor, PhD committee, family, friends, and everyone who has helped and supported me throughout this wonderful PhD journey 🤗.

[2023/12] I will present our work Leveraging Predictions in Power System Frequency Control: an Adaptive Approach at IEEE CDC 2023 in the session WeA16 Computational Techniques for Automation in Energy Systems. Welcome to drop by our talks.

[2023/12] I will present my research centers around structured control and learning for energy systems in the Meet the Faculty Candidates Poster Session at IEEE CDC 2023. Welcome to drop by our posters.

[2023/09] Our paper on Structured Neural-PI Control with End-to-End Stability and Output Tracking Guarantees is accepted in NeurIPS 2023.

[2023/06] Our paper on Leveraging Predictions in Power System Frequency Control: an Adaptive Approach and Bridging Transient and Steady-State Performance in Voltage Control: A Reinforcement Learning Approach With Safe Gradient Flow are accepted in IEEE CDC 2023.

[2023/05] Honored to be selected as a Rising Star in Cyber-Physical Systems (CPS).

[2023/04] I presented our work Efficient Reinforcement Learning Through Trajectory Generation in SIAM Conference on Applications of Dynamical Systems (DS23).

[2023/03] Our paper on A Frequency Domain Approach to Predict Power System Transients is accepted in the IEEE Transactions on Power Systems.

[2022/12] I presented our work Equilibrium-Independent Stability Analysis for Distribution Systems With Lossy Transmission Lines in IEEE CDC 2022.

[2022/10] Honored to be selected as a Rising Star in EECS.

[2022/09] Had a wonderful journey as a research intern at Microsoft Research. We worked on sample-efficient learning-based control algorithms to overcome the challenges of partial observability, sample complexity and the lack of real-time communication capability in real-world applications, including power and traffic networks.