I am a postdoctoral research assistant in computational statistics and machine learning at the University of Oxford supervised by Arnaud Doucet and funded by the CoSInES project. I recently have joined the algorithms and inference working group in Next Generation Event Horizon Telescope (ngEHT) collaboration to help improve the algorithms used to model and image supermassive black holes. Before this, I completed a PhD in Statistics with Alexandre Bouchard-Côté at the University of British Columbia.

My research aims to design algorithms for solving Bayesian inference problems in the applied sciences. I am interested in developing general-purpose algorithms that are mathematically grounded and can efficiently scale to modern computing resources. My work is interdisciplinary and lies at the interface between probability theory, statistical physics, machine learning, and differential geometry.

During my PhD I studied a non-reversible variant of a popular algorithm in computational statistics and physics called parallel tempering. We showed that the non-reversible parallel tempering (NRPT) dominates the traditionally used reversible counterpart, can be efficiently tuned and can scale to GPUs. See the applications section for recent examples of projects using NRPT at scale and the publications section to learn more about NRPT.