I am a Postdoctoral Fellow at Harvard Medical School, working with Chris Sander. Before Harvard, I completed my PhD with Amos Storkey in the Bayesian and Neural Systems Group at the University of Edinburgh. My doctoral thesis was on the geometry for deep representation learning.
My research lies at the intersection of machine learning and biomedicine, addressing two critical challenges in cancer biology: early detection using longitudinal patient records and optimization of combination therapies through mechanistic models of drug response. Late-stage diagnosis remains the primary cause of cancer mortality, as many patients present when curative treatment is no longer possible. By training AI on large-scale clinical data, our work aims to identify high-risk individuals who can benefit from earlier intervention and effective treatment strategies. My current work is centered around following topics:
Transforming longitudinal patient trajectories into structured embeddings that capture the evolving health state of patients and support downstream clinical decision-making tasks.
Developing well-calibrated deep learning event models that predict cancer risk across horizons while quantifying uncertainty at the patient level.
Modeling cellular dynamics with machine-learning driven differential equation models trained on drug and CRISPR perturbation data.