I am a Florence Nightingale Bicentennial Fellow in computational statistics and machine learning at the University of Oxford’s Department of Statistics, where I am also a member of the Algorithms and Inference Working Group for the Next Generation Event Horizon Telescope (ngEHT). Prior to this role, I completed a postdoc under the guidance of Arnaud Doucet and a PhD under the supervision of Alexandre Bouchard-Côté. My doctoral thesis won the Pierre Robillard Award from the Statistical Society of Canada (SSC), the Cecil Graham Doctoral Dissertation Award from the Canadian Applied and Industrial Mathematics Society (CAIMS), and the Savage Award (Honourable Mention) for theory and methods from the International Society for Bayesian Analysis (ISBA).
My research involves designing scalable and robust algorithms for Bayesian inference with scientific applications in mind. If you have a cool, computationally challenging problem reach out!
PhD in Statistics, 2022
University of British Columbia
MSc in Mathematics, 2016
University of British Columbia
BMath in Pure & Applied Mathematics, 2014
University of Waterloo
Many of the state-of-the-art algorithms in statistics, and machine learning, utilize a technique called annealing, which involves making inferences from an intractable target problem by incrementally deforming solutions from a tractable reference problem. I’m am interested in using annealing as a tool to understand the interplay between MCMC, SMC, variational inference, diffusion models, normalizing flows, and optimal transport.
Here are some recent examples of large-scale projects that used non-reversible parallel tempering (NRPT) as the backbone of their inference engine. Please reach out if you would like to use NRPT for your projects and have any questions. If you want to play around with NRPT check out our Julia package Pigeons.jl .