Tongxin Li

PhD Candidate in CMS at Caltech

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MC 305-16

Computing and Mathematical Sciences

Pasadena CA 91125

I am a doctoral candidate in CMS at the California Institute of Technology, co-advised by Dr. Steven H. Low and Dr. Adam Wierman. I am affiliated with the Caltech Rigorous Systems Research Group (RSRG) and Netlab. My research interests focus on interdisciplinary topics in control, learning and optimization for cyber-physical systems and artifical intelligence of things (AIoT). I devote myself to designing and developing artificial intelligence techniques that impact the sustainability and resilience of real-world networked systems. Some of my recent projects include learning-augmented decision-making, artificial intelligence and data science in smart grids. Prior to joining Caltech in 2017, I worked on various topics in communication and information theory at CUHK.

news

Mar 1, 2022 Our new paper about optimal phase-balanced EV charging has been accepted at PSCC 2022.
Oct 23, 2021 Talk in 2021 INFORMS Annual Meeting titled Learning-based Predictive Control via Real-time Aggregate Flexibility
Jul 15, 2021 Join AWS Security as a research scientist intern for three months
Jul 12, 2021 Check out two new papers on robustness and consistency for linear quadratic control and learning-based predictive control
Jun 15, 2021 Presentation of the paper about Information Aggregation in Constrained Non-linear Control in ACM SIGMETRICS 2021. Slides are available
Jul 1, 2020 Presentation of the paper about Electric Vehicle Charging Time Series Classification in XXI Power Systems Computation Conference PSCC2020
Jun 25, 2020 Presentation of the paper about Real-time Aggregate Flexibility in ACM e-Energy 2020. Slides are available
Jun 15, 2020 Join AWS Security as a research scientist intern for three months
Jul 4, 2019 Invited Talk Learning graph parameters from linear measurements: Fundamental trade-offs and application to electric grids in INFORMS-APS, Brisbane Australia

selected publications

  1. SIGMETRICS
    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
  2. 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
  3. SIGMETRICS
    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
  4. 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