ACE AI and Data Talk Series -Dr Luca Furieri

Closing the loop between optimal nonlinear control and learning-based optimization Abstract The increasing complexity of modern engineering systems demands new approaches to control and optimisation. Traditional methods provide fundamental theoretical guarantees but often struggle with scalability and adaptation to real-world uncertainties. Conversely, machine learning–based techniques achieve remarkable empirical performance but typically lack formal guarantees of stability and convergence. This talk introduces a recent unified approach to (1) neural-network control with stability guarantees and (2) learning linearly convergent algorithms for convex and non-convex optimisation. In the first part, we present a parametrisation of all and only those control policies that can stabilise a given time-varying nonlinear system. The main insight is that we can learn over a stable neural-network operator, thereby capturing exclusively the stabilising nonlinear control policies for a wide class of nonlinear systems, even under classes of model uncertainty. In the second part, we turn to convex and non-convex optimisation. While systems theory has established optimal worst-case linear convergence rates for convex functions, a recent trend in machine learning, Learning to Optimize (L2O), uses neural networks to discover update rules that excel even in non-convex scenarios. The catch is that formal convergence guarantees are generally not available. We bridge these two paradigms by developing a constructive characterisation of all linearly and asymptotically convergent algorithms for classes of smooth and non-smooth convex and non-convex functions. We illustrate the developed methods on optimal control benchmarks inspired by collision-avoidance problems and on optimisation benchmarks arising in image classification, ill-conditioned least-squares, and MPC. Bio Luca Furieri is an Associate Professor in the Department of Engineering Science at the University of Oxford, where he began in June 2025. His research focuses on optimal control and optimisation for distributed decision-making and large-scale cyber-physical systems. He received the Swiss National Science Foundation (SNSF) Ambizione career grant in 2022, the IEEE Transactions on Control of Network Systems Best Paper Award in 2022, and the American Control Conference O. Hugo Schuck Best Paper Award in 2018.