Note: I am currently on the job market, with a planned graduation date in summer 2022. Please feel free to reach out if you are interested in recruiting me! My email address can be found at the bottom of this page.
I am a 3rd 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 non-uniformly structured 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. To date, my work has won multiple best paper awards.
PhD in Computer Science, 2022 (expected)
University of Cambridge
MEng in Computer Science, 2019
University of Cambridge
BA in Computer Science, 2018
University of Cambridge
For full list, please refer to Google Scholar or DBLP
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.
Achieved 4.5x reductions in inference latency for models operating on point cloud data, without accuracy degradation. First author paper awarded best paper at the Deep Learning for Geometric Computing ICCV Workshop.
Worked with Partha Maji and Tiago Azevedo on the Machine Learning Research team.