PhD in Statistical Science, Oxford-Warwick Statistics Programme, 2020
University of Warwick, UK
BSc & MSc in Econometrics & Operations Research, 2016
Maastricht University, NED
I am a doctoral researcher working at the nexus of computer science and statistics within the Oxford-Warwick Statistics Programme (OxWaSP) supervised by Theodoros Damoulas. I am also a part of the Wariwck Machine Learning Group, visiting scholar at Duke University as well as a visiting researcher at the Alan Turing Institute for Data Science and AI in London where I am affiliated with the London air quality project to support London’s Major’s office with data-driven policy. More information can be found in my (outdated) CV.
My interests revolve around scalable inference methods for spatio-temporal data streams that can run in real time. Inference for complex dynamical systems generating high-dimensional structured data is typically complicated by non-stationarity, changepoints, model uncertainty, misspecification and outliers. While the analysis of real-world data streams almost always needs to address these complications, tackling them jointly leads standard likelihood-based learning rules to break down. My mission is to work on alternative learning rules derived from generalized Bayes theorems which can solve this collection of problems jointly, efficiently and effortlessly. A more detailed overview over what inspires me is available here.