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.


  • [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

Graph Learning and Causal Inference