I am a Schmidt postdoctoral fellow at the Broad Institute of MIT & Harvard. I work with Prof. Caroline Uhler at MIT.
​
My research interests are broadly in probabilistic machine learning methodologies like Gaussian processes and kernel design. I actively work in scientific applications of machine learning to problems in contemporary sciences like computational biology, drug-discovery and astronomy. I currently work on generative models for small molecules and the evaluation of foundational models for representation learning pipelines in molecular machine learning and single-cell genomics.
​
I completed my PhD at the University of Cambridge (UK) in 2024. I was based at the Cavendish Laboratory (Physics) and the Computational & Biological Learning Lab at the Dept. of Engineering. During my time in Cambridge I was a Turing Scholar and a member of Christ’s College. I was awarded a G-Research PhD prize for my thesis and a Qualcomm Innovation Fellowship. 
​​
I was supervised by Prof. Carl Rasmussen and Prof. Neil Lawrence at Cambridge. I also hold a MPhil in Scientific Computing from the University of Cambridge (Distinction), an MSc in Applicable Mathematics from the LSE (Distinction). I did my undergraduation in Mathematics (major) and Economics as an external student of the Univeristy of London (LSE).​​​​