I am a Schmidt Futures AI2050 Early Career Fellow at the University of Oxford in the Department of Computer Science. My work is centered around scalable and flexible methods for spatiotemporal statistics and Bayesian machine learning with applications in epidemiology. Currently, my focus is on using deep generative modelling to power MCMC inference in classical spatial statistics, as well as adaptive survey design.
Previously, I worked at Imperial College London, Department of Mathematics, Statistics section (2021-2022) and did a postdoc in Bayesian Machine Learning at AstraZeneca R&D (2019-2021) where I also collaborated with Prioris.ai. My research at AstraZeneca was dedicated to toxicity prediction and concentration-response curve fitting of large molecules using changepoint Gaussian Processes.
In 2019 I completed a PhD in Epidemiology at the Swiss TPH, where I worked on modelling of point pattern data using Log-Gaussian Cox Process and detection of hotspots on gridded surfaces.
My most recent organisational activities include (1). ICLR'23 “First workshop on Machine Learning & Global Health”; (2). NeurIPS'22 “Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems”; (3). Gaussian Processes seminar series; (4). Data Science Theme Ambassador at Imperial College London.
Download my resumé.
For more about my work, see the list of my recent publications and talks.
PhD (summa cum laude) in Epidemiology, 2019
Swiss Tropical and Public Health Institute (TPH), University of Basel, Switzerland
Diploma (first class honours) in Mathematics, 2008
Moscow State University, Russia