Zhenyi Yuan's Homepage
Short BiographyI am currently a researcher affiliated with CUHK Information Engineering department, conducting research on power systems control with Prof. Changhong Zhao. I earned the Ph.D. degree in Engineering Sciences (Mechanical Engineering) from UC San Diego, where I was advised by Prof. Jorge Cortés. During my Ph.D., I worked closely with academic researchers from NREL, CUHK, UW Madison, Oxford, ETH Zürich, etc., and was involved in several industrial projects collaborating with NREL and TotalEnergies. Prior to Ph.D., I was enrolled in the successive undergraduate and postgraduate program at the Honors School of Harbin Institute of Technology, where I received the B.S. (w/ honors) and M.S. degrees in Control Science and Engineering under the supervision of Prof. Ligang Wu. Recent Updates/Upcoming Events[Highlight] I am on the 25-26 academic job market! [To CUHK Students] (especially those who aspire to pursue a PhD in systems & control): Feel free to contact me for scheduling office hours, I am more than happy to provide anything I can help. Earlier Updates[09/2025] Paper accepted to Transactions on Smart Grid, where we close our series of works on provably stable voltage control with a unified framework, see here. [08/2025] Forum talk at the School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen. [07/2025] Seminar talk at the School of Data Science, The Chinese University of Hong Kong, Shenzhen. [07/2025] Paper accepted to CDC'25, where we provide the fundamental lemma for nonlinear systems admit Koopman bilinear realization, see here. [12/2024] Visited and gave a talk at IfA of ETH Zürich; attended the "Meet the Faculty Candidates Poster Session" at CDC in Milan. [07/2024] Did a talk at Shanghai Jiao Tong University (SJTU) about the overview of our series works on Provably Stable Voltage Control. [06/2024] Attended the PSCC in Paris, lots of fun and, particularly, many enjoyable discussions with fascinating ideas. See From the Conference here! Research InterestsControl, Optimization and Machine Learning, as well as their applications to Electric Power Systems.
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