Tongxin Li

Assistant Professor, CUHK-Shenzhen


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 machine learning techniques that improve the sustainability, robustness, scalability, privacy, and resilience of intelligent energy infrastructure. 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

  • Learning-augmented online control and algorithms

  • AI for sustainability

  • AI in energy systems

  • Trustworthy machine learning in cyber-physical systems

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.


Nov 19, 2022 I received the SIGEnergy Doctoral Dissertation Award Honorable Mention.
Oct 28, 2022 Algorithms with Predictions Seminar Course Talk. Slides: Link
Oct 22, 2022 2022 INFORMS Annual Meeting. Slides: Link
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

selected publications

  1. PSCC
    Towards balanced three-phase charging: Phase optimization in adaptive charging networks
    Ye, Zixin,  Li, Tongxin, and Low, Steven
    Electric Power Systems Research 2022
    Robustness and Consistency in Linear Quadratic Control with Untrusted Predictions
    Li, Tongxin, Yang, Ruixiao, Qu, Guannan, Shi, Guanya, Yu, Chenkai, Wierman, Adam, and Low, Steven
    Proceedings of the ACM on Measurement and Analysis of Computing Systems 2022
  3. TSG
    Learning-based Predictive Control via Real-time Aggregate Flexibility
    Li, Tongxin, Sun, Bo, Chen, Yue, Ye, Zixin, Low, Steven H, and Wierman, Adam
    IEEE Transactions on Smart Grid 2021
    Information Aggregation for Constrained Online Control
    Li, Tongxin, Chen, Yue, Sun, Bo, Wierman, Adam, and Low, Steven H
    Proceedings of the ACM on Measurement and Analysis of Computing Systems 2021
  5. TSIPN
    Learning graphs from linear measurements: Fundamental trade-offs and applications
    Li, Tongxin, Werner, Lucien, and Low, Steven H
    IEEE Transactions on Signal and Information Processing over Networks 2020