Contact
Biography
Homepage: https://anthony-s-chen.github.io/
Dr. Anthony Siming Chen earned his B.Eng. degree from Central South University, China in 2015, graduating with highest honours as a selected member of the Shenghua Honors College and the National Advanced Engineering Talent Programme. During his undergraduate studies, he undertook a one-year industrial placement at CRRC. After relocating to England in the summer of 2015, he obtained his M.Sc. and Ph.D. degrees from the University of Bristol in 2017 and 2022, respectively, as part of the Dynamics and Control Group.
He is now an Assistant Professor with the Department of Electrical and Electronic Engineering at the University of Nottingham, U.K.. He is a member of the Mechanical and Aerospace Systems Research Group (MAS), the Roll-Royce UTC on Gas Turbine Transmission Systems, and the Institute for Aerospace Technology (IAT). He also holds an honorary position with the Control Systems and Robotics Group (CSR) and the Centre for Robotics and AI at the University of Manchester, U.K.. Prior to joining Nottingham, he was a Postdoctoral Research Associate at the University of Manchester (2022-2025), and a Visiting Researcher at the Institute for Advanced Automotive Propulsion Systems (IAAPS), University of Bath (2016-2022).
Dr. Chen is a RAICo Fellow, MIEEE, MIET, and Member of the Royal Aeronautical Society (MRAeS). He serves on multiple IEEE Technical Committees, including the IEEE Control Systems Society (CSS) Technical Committee on Quantum Computing, Systems, and Control, the IEEE Robotics and Automation Society (RAS) Technical Committee on Aerial Robotics and Unmanned Aerial Vehicles, IEEE RAS Technical Committee on Robot Learning, and IEEE RAS Technical Committee on Robot Control. He publishes and reviews papers in major venues: Control (e.g., TAC, Automatica, TCST, CDC, ACC), Robotics (e.g., TRO, TMECH, RAL, ICRA, IROS), and Aerospace (e.g., AST, TAES, TVT, AIAA SciTech).
Expertise Summary
Dr Chen's research sits at the intersection of control theory, machine learning, and robotics, with a particular focus on embodied intelligence and learning-based methods for complex real-world systems. His work spans applications in UAVs, aero-engines, robotics, and quantum systems.
Keywords:
Control Theory
Reinforcement Learning
Robotics
Aerospace
Embodied Intelligence