Dao Yuan Building, 323A
School of Data Science
I am currently a tenure-track assistant professor in the School of Data Science (SDS) at The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen).
Prior to joining SDS, I received a PhD in CMS at the California Institute of Technology, co-advised by Dr. Steven H. Low and Dr. Adam Wierman. I graduated from CUHK in a dual-degree program and obtained a BEng in information engineering, a BSc in mathematics and an MPhil in information engineering. I interned twice as an applied scientist at AWS security in the summers of 2020 and 2021.
My research interests focus on interdisciplinary topics in machine learning, control, and optimization, with applications to power systems and sustainability. In particular, I am interested in developing trustworthy artificial intelligence and machine learning techniques that improve the sustainability, robustness, scalability, privacy, and resilience of smart grids. Some of my recent projects include learning-augmented control, data science and ML methods in smart grids.
RECRUITMENT ANNOUNCEMENT: I am actively looking for PhD students, research assistants, and postdoctoral researchers who are interested in contributing to the emerging domains of
Trustworthy machine learning
AI for sustainability
Clean energy systems
Learning-based control and online learning
Several positions for fully-funded postdocs/graduate students/undergraduate interns focusing on the study of machine learning, power systems, control, and optimization are available. Feel free to send me an email with your CV attached. Other formats of local and remote collaboration are also welcomed.
|Mar 1, 2022||Our new paper about Optimal Phase-Balanced EV Charging has been accepted at PSCC 2022.|
|Oct 24, 2021||Talk in 2021 INFORMS Annual Meeting titled Learning-Based Predictive Control via Real-Time Aggregate Flexibility|
|Jul 16, 2021||Join AWS Security as a research scientist intern for three months|
|Jul 13, 2021||Check out two new papers on Robustness and Consistency for Linear Quadratic Control and Learning-Based Predictive Control|
|Jun 16, 2021||Presentation of the paper about Information Aggregation in Constrained Nonlinear Control in ACM SIGMETRICS 2021. Slides are available|
|Jul 2, 2020||Presentation of the paper about Electric Vehicle Charging Time Series Classification in XXI Power Systems Computation Conference PSCC2020|
|Jun 26, 2020||Presentation of the paper about Real-Time Aggregate Flexibility in ACM e-Energy 2020. Slides are available|
|Jun 16, 2020||Join AWS Security as a research scientist intern for three months|
|Jul 5, 2019||Invited Talk in The 20th INFORMS Applied Probability Society Conference (INFORMS-APS 2019), Brisbane Australia|
PSCCTowards balanced three-phase charging: Phase optimization in adaptive charging networksElectric Power Systems Research 2022
SIGMETRICSRobustness and Consistency in Linear Quadratic Control with Untrusted PredictionsProceedings of the ACM on Measurement and Analysis of Computing Systems 2022
TSGLearning-based Predictive Control via Real-time Aggregate FlexibilityIEEE Transactions on Smart Grid 2021
SIGMETRICSInformation Aggregation for Constrained Online ControlProceedings of the ACM on Measurement and Analysis of Computing Systems 2021
TSIPNLearning graphs from linear measurements: Fundamental trade-offs and applicationsIEEE Transactions on Signal and Information Processing over Networks 2020