John Cao

PhD Student

Bio: John is a DPhil student in the group of Professor Luca Furieri at the University of Oxford. His research focuses on learning-based control, combining state-of-the-art machine learning methods with control theory. He holds a bachelor’s degree from KTH Royal Institute of Technology and a master’s degree from Stanford University, both in Electrical Engineering. Previously, he conducted research at KTH, Caltech, Stanford, and NASA. Outside of work, he is often found working out in the gym or on the climbing wall.

Research interests: Learning-based control & optimization · Machine learning · Safe reinforcement learning

Links: LinkedIn

Junan Lin

PhD Student

Bio: Junan completed a master’s degree in the Robotics, Systems and Control program at ETH Zürich in 2025, and a bachelor’s degree at Beihang University in 2022. His research focuses on incorporating machine learning techniques into control schemes to make them more adaptive, secure, and computationally efficient. His master’s thesis explored efficient learning of value functions for constrained LQR using differentiable optimization.

Research interests: Differentiable optimization · Learning based control & optimization · Scalable methods

Links: Personal webpage

Daniele Martinelli

PhD Student

Bio: Daniele received the B.Sc. degree in Automation and Control Engineering from the Università degli Studi di Napoli Federico II, Naples, Italy, in 2020, and the M.Sc. degree in Automation and Control Engineering from Politecnico di Milano, Italy, in 2022. He is currently pursuing a Ph.D. degree in Robotics, Control, and Intelligent Systems with the Automatic Control Laboratory at the École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. His research interests include networked control theory, optimization, and machine learning, with a focus on developing scalable algorithms for large-scale systems. When not in the lab, he can often be found playing music, cooking Italian dishes, or climbing.

Research interests: Networked control · Optimization · Large-scale systems · Machine learning for control

Links: LinkedIn · Google Scholar

Yuhang Ye

PhD Student

Bio: Yuhang received an M.Sc. degree in Control and Optimisation from Imperial College London in 2024 and a B.Eng. degree in Automation from China University of Geosciences (Wuhan) in 2023. He is currently a DPhil student in the Control Group at the University of Oxford. His research lies at the intersection of machine learning and control theory, including neural network control, reinforcement learning, and learning-based control, with a focus on ensuring the safety and reliability of these methods.

Research interests: Safe reinforcement learning · Learning-based model-predictive-control · Constrained optimization

Links: LinkedIn