I am a lecturer in Biostatistics, Computational Epidemiology and Machine Learning at Imperial College London, Department of Epidemiology and Biostatistics. And I also hold Schmidt Sciences AI2050 Early Career Fellowship.

My work is centered around scalable and flexible methods for spatiotemporal statistics and Bayesian machine learning with applications in epidemiology. Most recently, my focus has been on using deep generative modelling to power MCMC inference in classical spatial statistics, as well as adaptive survey design.

Previously I worked at the University of Oxford, Computer Science (2022-2024) and Imperial College London, Department of Mathematics, Statistics section (2021-2022) with Seth Flaxman and MLGH network. Before that I did a postdoc in Bayesian Machine Learning at AstraZeneca R&D (2019-2021) where I also collaborated with Prioris.ai.

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). StanCon 2024; (2). ICLR'23 “First workshop on Machine Learning & Global Health”; (3). NeurIPS'22 “Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems”; (4). Gaussian Processes seminar series; (5). Data Science Theme Ambassador at Imperial College London.

Download my resumé.