I am a 2nd year PhD student in Machine Learning at the University of Cambridge, having started in October 2019. I am a member of the Machine Learning Systems group in the Computer Science department, where I am supervised by Dr Nic Lane.
My research interests focus on enabling edge devices to reason from data efficiently. The research in my PhD so far has focused on enabling efficient inference for graph neural networks (GNNs), which have a variety of useful applications which are currently not viable for edge platforms. I am interested in hardware-software co-design techniques, systems challenges, and applications.
My Bachelor's dissertation with Dr Robert Harle investigated anonymous human proximity detection techniques for smartphones; similar approaches are now used for COVID-19 contact tracing. In my Master's dissertation, with Professor Cecilia Mascolo, I focused on building devices that can listen to your body, and won a highly commended award from the department, and a best paper award at the WellComp workshop at Ubicomp 2020.
PhD in Computer Science, 2023 (expected)
University of Cambridge
MEng in Computer Science, 2019
University of Cambridge
BA in Computer Science, 2018
University of Cambridge
Explored the viability of capturing internal body sounds with a wearable device for medical applications. This project involved the construction of wearable device (with specialised acoustic circuitry) which was used to collect a dataset; the experimental procedure was designed to evaluate performance during challenging user activities and acoustic conditions. Algorithms for continuous heart monitoring and asthma symptom detection are described and evaluated. It was shown that the algorithms could be run in real-time on plausible wearable hardware.
Supervised by Professor Cecilia Mascolo. Awarded “Highly Commended” dissertation prize by the University. Work from dissertation published at WellComp workshop at UbiComp 2020, winning best paper.