Biography

I am an Assistant Professor and Presidential Young Fellow at the School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), where I lead the ENLIGHT Lab (ENergy analyticaL insIGHTs). Our lab develops data-driven, optimization-based, and learning-enabled methodologies for modern power and energy systems.

Education

I received my B.S. degree in Electronic Engineering from Tsinghua University in 2009, and my Ph.D. degree in Computer Science and Engineering from Tsinghua University in 2013, under the supervision of Prof. Andrew Yao, Turing Award Laureate.

Roles & Service

I currently serve as Assistant Dean (Education) of the School of Science and Engineering at CUHK-Shenzhen, and Assistant Dean of the Shenzhen Loop Area Institute, overseeing admissions, education, and internationalization.

I serve as an Editorial Board Member of IEEE Systems Journal, IEEE Transactions on Smart Grid, and IEEE Power Engineering Letters, and as TPC Co-Chair of IEEE SmartGridComm 2025 and 2026.

Research & Recognition

My research aims to develop a new paradigm for power grid operation by integrating power system principles with tools from computer science, data analytics, optimization, and machine learning — designing methods that are not only practically effective but also theoretically grounded.

I have published over 100 papers in leading journals and conferences, including IEEE Transactions on Power Systems, IEEE Transactions on Smart Grid, and IEEE Transactions on Sustainable Energy.

Smart Meter Privacy

"Does preserving smart meter privacy inevitably destroy data utility for grid operations?"

I study privacy-preserving methods for smart meter data analytics, aiming to mathematically break the apparent zero-sum tradeoff between consumer privacy and operational value.

Robust Grid Operation

"Must uncertainty-aware dispatch be overly conservative or computationally prohibitive?"

I develop robust and data-efficient operation methods that handle renewable uncertainty and system variability using limited real-world samples, while maintaining practical computational efficiency.

Market Design

"Are distribution-level local energy markets too complex to implement due to non-convex power flows?"

I investigate market mechanisms and pricing methods for future distribution systems, including distributed marginal pricing enabled by advanced power electronics and flexible grid resources.

Learning for Operation

"Does the most accurate load predictor always lead to the most cost-efficient operation?"

I examine the connection between prediction accuracy and operational cost, and design learning methods that directly optimize downstream decision-making rather than prediction metrics alone.

Sample-Efficient Reinforcement Learning

"Is the curse of dimensionality unavoidable for reinforcement learning in large-scale energy systems?"

I study reinforcement learning methods that exploit the physical modularity and network structure of power grids, improving sample efficiency and scalability with theoretical guarantees.