About me

I am a doctoral student 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. I devote myself to design and develop artificial intelligence techniques that impact the sustainability and resilience of real-world networked systems. Some of my recent works include learning and inferring graphs from network data, ACN (adaptive EV charging network) control using data-driven methods and information aggregation for online control. Prior to joining Caltech in 2017, I worked on various topics in communication and information theory such as group testing, deletion and adversarial channels, etc. Under the supervision of Prof. Sidharth Jaggi, I received two bachelor degrees in both mathematics and information engineering from The Chinese University of Hong Kong.

Updates

• [July 1st, 2020] Presentation of the paper about Electric Vehicle Charging Time Series Classificvation in XXI Power Systems Computation Conference PSCC2020

• [June 25th, 2020] Presentation of the paper about Real-time Aggregate Flexibility in ACM e-Energy 2020. Slides are available

• [June 15th, 2020] Join AWS Security as a research scientist intern for three months

• [December 12th, 2019] Slides for the presentation of the paper on learning power networks using linear measurements in CDC 2019 are available

• [July 4th, 2019] Invited Talk “Learning graph parameters from linear measurements: Fundamental trade-offs and application to electric grids” in The 20th INFORMS Applied Probability Society Conference (INFORMS-APS 2019), Brisbane Australia

• [April 27th, 2019] Talk “Model-based graph learning with linear measurements” in Southern California Applied Mathematics Symposium (SOCAM 2019), Pasadena, USA

Selected Publications and Preprints

Data-driven EV Charging Network

• Real-time Flexibility Feedback for Closed-loop Aggregator and System Operator Coordination
  Tongxin Li, Steven H. Low and Adam Wierman. (e-Energy ’20) Proceedings of the Tenth ACM International Conference on Future Energy Systems. 2020.
• ORC: An Online Competitive Algorithm for Recommendation and Charging Schedule in Electric Vehicle Charging Network
  Bo Sun, Tongxin Li, Steven H. Low and Danny H.K. Tsang. (e-Energy ’20) Proceedings of the Tenth ACM International Conference on Future Energy Systems. 2020.
• Classification of Electric Vehicle Charging Time Series with Selective Clustering
  Tongxin Li, Chenxi Sun (equal contribution), Steven H. Low and Victor O.K. Li. accepted by XXI Power Systems Computation Conference (PSCC). 2020.
• ACN-Data: Analysis and Applications of an Open EV Charging Dataset
  Zachary J. Lee, Tongxin Li, and Steven H. Low. e-Energy ’19 Proceedings of the Tenth ACM International Conference on Future Energy Systems. 2019.

Graph Learning and Causal Inference

• Learning Graph Parameters from Linear Measurements: Fundamental Trade-offs and Applications
  Tongxin Li, Lucien Werner and Steven H. Low. IEEE Transactions on Signal and Information Processing over Networks, 6 : pages 163–178,. 2020.
• Learning Graph Parameters from Linear Measurements: Fundamental Trade-offs and Application to Electric Grids
  Tongxin Li, Lucien Werner and Steven H. Low. 58th IEEE Conference on Decision and Control (CDC). 2019.
• Disentangling Causal Effects from Latent Confounders using Interventions
  Hao Liu, Anqi Liu, Tongxin Li, and Animashree Anandkumar. accepted by NeurIPS Workshop on causal inference. 2019.