PhD Student Bio

I am a PhD candidate in Bayesian Machine Learning at the Helmholtz Institute in Munich where I am supervised by Dr. Vincent Fortuin. As part of the ELLIS program, I am also supervised by Prof. Jose Miguel Hernandez-Lobato at the University of Cambridge.

My broad interest lies in achieving human-like intelligence, especially in regards to dealing with uncertainty and adaptability to new circumstances. I believe Bayesian methods present a principled approach to these questions as well as exciting avenues for research. Specifically, I think about fast and scalable approximate inference algorithms, continual learning approaches, meta learning and other related topics. Alongside Bayesian ML, I am deeply interested in geometric machine learning, reinforcement learning, optimization, and generative AI.

I hold a Master’s degree in Mathematics and Computer science from the University of Oxford. Before starting my PhD, I worked for several years as a software/quant developer. In this role I built fleet management systems for AVs as well as trading software for a cryptocurrency options desk. Most recently, I worked as a quant researcher at a small hedge fund. There, I researched machine learning methods for trading signals generation while also working on the MLOps pipeline for recomputing the signals when new data became available. Besides the roles at established companies, I was also an early developer/founder at several startups.