I am currently a postoctoral fellow in the School of Engineering and Applied Sciences at Harvard University, working with Professor Na Li. I received my B.S. degree in Eletronic Engineering from Tsinghua University in 2013. I received my Ph.D. degree in Electrical Enginnering from the California Institute of Technology in 2019, under the supervision of Professor Steven Low.

 
The main theme of my research is to integrate optimization theory, control theory, learning, and networked systems in a theoretically sound and computationally tractable manner, to address the various challenges in large-scale cyber-physical networks. Particularly, my past and current research has developed novel algorithms and analysis tools for cyber-physical networks from the perspective of cooperative multi-agent systems, with the aim of addressing the following two challenges:
  1. Lack of model information. In many real-world scenarios, decision makers may not have access to a complete and accurate model describing the mechanism of the physical layer, which may be due to the complexity of the physical mechanism or lack of data for inference of such a model. To deal with the issue of lack of model information, we have exploited recently developed tools from zeroth-order optimization and tailored them to match specific observation and communication restrictions in cyber-physical networks.
  2. Nonstationary and time-varying components. For many cyber-physical networks, the physical layer can be subject to the influence of exotic nonstationary and time-varying components that are hard to predict a priori, which imposes further challenges that call for the development of new algorithms and theories. We have developed theories and algorithms for time-varying nonconvex optimization problems, with applications in smart grids that lead to real-time optimal power flow algorithms.

My goal is to build an interdisciplinary research program to develop advanced optimization, control and learning methods algorithms for cyber-physical networks that are scalable, adaptive to uncertainties, robust to disturbances, and with guaranteed optimality, by integrating both theoretical tools and engineering insights.

 

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