Biography

I was first exposed to AI research during my MSc at Imperial and later at my MPhil at Cambridge, where I focused on explainable AI. My work showed that models can achieve high performance while relying on spurious correlations, revealing a gap between accuracy and trustworthiness. I wanted to address this gap beyond research and build systems that improve people’s lives. Motivated by this, I co-founded Tenyks and led deployments of AI systems across hospitals, care homes, and QSRs. In healthcare, our systems monitor patients in real time and have detected falls and early signs of stroke during night shifts, enabling immediate intervention. Deploying these systems also made clear that, in many real-world settings, data are distributed across institutions, highly sensitive, and often left untapped because they cannot be easily shared. Federated learning is compelling because it enables collaborative training across such data while preserving privacy. At the same time, it introduces new challenges around robustness, fairness, and interpretability. These challenges shaped my goal to work on trustworthy federated learning during my PhD.

Previous Education

University of Cambridge Advanced Computer Science 2021
Imperial College London (University of London) Artificial Intelligence 2020