Matthew completed a master’s degree (MEng) in Engineering Science at the University of Oxford. His master's thesis, supervised by Prof. Stephen Duncan, investigated the stability of pipe flow. In addition to this he completed an internship at the Oxford Man Institute of Quantitative Finance, computing higher order dependencies between features in unstructured financial data sets.
Matthew is a student on the AIMS-CDT program and is now a member of the Control Group, supervised by Prof. Antonis Papachristodoulou, where he completed two mini-projects on population dynamics and autonomous vehicle control. His research areas include analysing epidemic models and finding more efficient and accurate methods to verify the robustness of neural networks.
He is a college lecturer for Worcester College, where he teaches 1st and 2nd year undergraduate students and is a departmental teaching assistant for 3rd year undergraduate students.
- Epidemic Models
- Neural Networks
- M. Newton and A. Papachristodoulou, “Exploiting Sparsity of Neural Network Verification,” 3rd Annual Learning for Dynamics and Control Conference, 2021
- M. Newton and A. Papachristodoulou, "Network Lyapunov Functions for Epidemic Models," 2020 59th IEEE Conference on Decision and Control (CDC), Jeju, Korea (South), 2020, pp. 1798-1803, doi: 10.1109/CDC42340.2020.9304021.