Dr. Xin Chen
Assistant Professor
Energy and Power Group
Department of Electrical and Computer Engineering
Texas A&M University
Email: xin_chen@tamu[dot]edu
Office: WEB 215F
Phone: +1 (979) 845-7469
[Google Scholar]
[Our group is seeking highly self-motivated students at all levels with research passion and strong mathematical backgrounds to join us. In particular, we have multiple fully funded Ph.D. positions available, starting in Spring 2025 and Fall 2025. Students with backgrounds in power systems, control theory, machine learning, optimization, mathematics, and related fields are encouraged to apply. For more details, click here]
Dr. Xin Chen is an Assistant Professor in the Department of Electrical and Computer Engineering at Texas A&M University. Dr. Chen directs the Smart Power, Energy and Decision-making (SPEED) Lab at TAMU ECE. The research of SPEED lies in the intersection of control, machine/reinforcement learning, and optimization for human-cyber-physical systems, with particular applications to sustainable power and energy systems. The SPEED lab aims to develop fundamental theories, scalable decision-making algorithms, and practically applicable tools to advance the intelligence, resilience, and sustainability of modern power and energy systems.
Dr. Chen received the Ph.D. degree in electrical engineering from Harvard University (working with Prof. Na Li), the master’s degree in electrical engineering and two bachelor’s degrees in engineering and economics from Tsinghua University. Prior to joining TAMU, Dr. Chen was a Postdoctoral Associate affiliated with MIT Energy Initiative at Massachusetts Institute of Technology, working with Prof. Andy Sun. Dr. Chen is a recipient of the IEEE PES Outstanding Doctoral Dissertation Award, IEEE Transactions on Smart Grid Top-5 Papers, the Best Research Award at the 2023 IEEE PES Grid Edge Conference, the Outstanding Student Paper Award at the 2021 IEEE Conference on Decision and Control, the Best Student Paper Award Finalist at the 2018 IEEE Conference on Control Technology and Applications, and the Best Conference Paper Award at the 2016 IEEE PES General Meeting.