Tongxin Li
PhD Candidate in CMS at Caltech

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. |
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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 |