I am an Eric and Wendy Schmidt Center (EWSC) postdoctoral fellow at the Broad Institute of MIT & Harvard. I work with Prof. Caroline Uhler at MIT and EWSC.  

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My research interests are broadly in Bayesian non-parametric models like Gaussian Processes and probabilistic methodologies for machine learning. Bayesian non-parametrics is an important paradigm in building artificially intelligent systems that allows the user to stipulate a prediction in terms of a probability distribution and allows automatic calibration of model complexity.

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I actively work in scientific applications of probabilistic machine learning to problems in contemporary sciences like computational biology, high energy physics and astronomy. I currently work on generative models for small molecules in the space of drug-discovery and the evaluation of foundational models for discovery pipelines in molecular machine learning and single-cell RNA seq data. 

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I completed my PhD at the University of Cambridge (UK) in 2023. 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 in 2023 for my thesis and a Qualcomm Innovation Fellowship in 2020. 

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I was supervised by Prof. Carl Rasmussen and Prof. Neil Lawrence both at Cambridge. I also hold a MPhil in Scientific Computing with Distinction (Cambridge), MSc. in Applicable Mathematics with Distinction (LSE) and a BSc. in Mathematics with First Class Honors (Univesity of London, External). 

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Education

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