About me

Hi! I am a PhD student in (Applied) Mathematics at Harvard University, where I am fortunate to be advised by Melanie Weber in the Geometric Machine Learning group. My work is supported by a Graduate Research fellowship from Harvard’s Kempner Institute for Natural and Artificial Intelligence.

My research focuses on Geometric Deep Learning and its applications in Biomedical AI. Specifically, I study Machine Learning on non-Euclidean domains, such as graphs, hypergraphs, and manifolds. My goal is to leverage geometric and topological tools to advance AI’s capability in handling structured, high-dimensional data. This has particularly promising applications in Biomedical AI, such as modeling molecular structures in drug discovery, analyzing complex patient networks for personalized medicine, and predicting interactions between different drugs.

Prior to Harvard, I received my Master’s in Mathematics with Distinction from Trinity College, University of Oxford, where I was supervised by Harald Oberhauser and Renaud Lambiotte. My work at Oxford focused on Stochastic Analysis and applications of Geometry in Network Science, exploring mathematical frameworks that inform understanding of dynamic and networked systems. You can view a current version of my CV here.

I am always happy to meet new people! Feel free to reach out on Twitter or via email.