Co-design AI learning algorithms and learning theoretic guarantees so to provide deep learning systems with strong empirical performance as well as theoretical guarantees.
The central aim of this research is to investigate how foundation models can be effectively leveraged to enhance decision-making capabilities in single autonomous agents operating within complex, real-world environments.
Humans with experience are very able to know when something is going wrong with the system they are using and to compensate for it. In autonomous system this intuition needs to be built into the algorithmic design.
The objective is to mix filtering methods with optimisation and deep learning to design advanced efficient tracking methodologies, with strong theoretical guarantees.
This project will develop novel Artificial Intelligence (AI) techniques for position location, considering both the algorithm’s performance and its ability to be implemented efficiently in hardware.
We will apply state-of-the-art signal processing techniques (e.g. online sequential Bayesian inference, reinforcement learning) to make spin-based quantum sensors faster, more robust against noise and changing environments, and more user-friendly, with the goal of detecting weak nanoscale magnetic resonance signals from small ensembles of molecules.
