I am supervised by Prof. Carl Rasmussen and Dr. Christopher Lester. My research interests are in Bayesian non-parametric models like Gaussian Processes and Dirichlet processes. I am interested in applications of probabilistic machine learning to problems in contemporary sciences like computational biology, high energy physics and astronomy. Uncertainty quantification in predictions is becoming increasingly mainstream as several applications in science and industry require statistical guarantees in their predictions. Bayesian non-parametrics is a leading paradigm that allows the user to stipulate a prediction in terms of a probability distribution and allows automatic calibration of model complexity. My Ph.D. is funded by the Alan Turing Institute and Qualcomm Innovation Fellowship (Europe) which I was awarded in 2020.
Past: Before joining Cambridge in 2016 I worked in algorithmic trading for global electronic FX markets at Credit Suisse and as a high frequency prop trader for (pan-European equities book) at the Chicago-based hedge fund, Citadel Securities (Europe) between 2011 and 2015.
Hierarchical Probabilistic Models
Latent Variable Models