Update as of 09/2022: I have joined the Department of Industrial Engineering & Management at Peking University as an assistant professor. Please visit my new homepage at tyj518.github.io, and contact me via yujietang[AT]pku.edu.cn
I was 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.
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Lack of model information. It is common in real-world applications that decision makers may not have access to a model describing the mechanism of the physical system. To deal with this issue, we have exploited tools from zeroth-order optimization and developed novel distributed optimization and reinforcement learning algorithms, with rigorous theoretical analysis on their performance.
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Partial observability. In many practical situations, the decision-making agents will only receive partial observation of the system’s state. So far, our understanding of partially observable systems has been limited. I am interested in developing theories and algorithms that can push forward our understanding on model-free reinforcement learning for partially observable systems.
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Nonstationary and time-varying components. For many cyber-physical networks, the physical layer can be subject to the influence of exogenous nonstationary and time-varying components that are hard to predict. 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.