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.
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.
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.