Morgan Jones

Dr. Morgan Jones is a Lecturer in Machine Learning and Control Theory at the University of Sheffield's School of Electrical and Electronic Engineering. He holds a B.S. and MMath in Mathematics from the University of Oxford (2016), and a PhD in Aerospace Engineering from Arizona State University (2021). His research focuses on dynamic programming, reinforcement learning, optimal control, and nonlinear systems analysis, with applications to battery scheduling, path planning, and obstacle avoidance. He is affiliated with the Cybernetic Systems and Controls Laboratory (CSCL) at Arizona State University, where he conducted research from 2016 to 2021.

Research interests include developing convex optimization tools for solving Hamilton-Jacobi-Bellman (HJB) PDEs, approximating regions of attraction and invariant sets using sum-of-squares (SOS) programming, and advancing methodologies for controller synthesis and system stability analysis. His work bridges theoretical control systems with practical applications in robotics, energy systems, and autonomous navigation.

Dr. Jones has published extensively in top-tier journals and conferences, including IEEE Transactions on Automatic Control, Automatica, and the American Control Conference (ACC). His recent contributions address sublevel set approximation, sparse identification of nonlinear dynamics, and bounding controller performance via value function analysis. He teaches advanced systems engineering courses and advises on PhD research in control theory.