From May 2021 until January 2025, I was a quantitative researcher at London-based hedge fund Eisler Capital, working in their Cambridge office on systematic trading strategies.
Prior to that, I was a machine learning research team lead at Secondmind (formerly PROWLER.io), a Cambridge-based start-up for autonomous decision-making in industrial applications. My team and I developed Bellman, an open-source software library for model-based reinforcement learning with the aim to support basic research and customer-related applications. We were also partially involved in another open-sourcing effort leading to GPflux, a library for Bayesian deep learning covering Bayesian neural networks and deep Gaussian processes to provide flexible probabilistic models for approximate inference.
In the past, my basic research at Secondmind mostly focused on reinforcement learning with specific emphasis on information-theoretically motivated algorithms, but I was also involved in research around Bayesian deep learning. On the applied side, I was involved in a couple of customer-related projects, e.g. developing a model-based reinforcement learning agent for systematic trading, creating a framework for tuneable artificial intelligence, and project-leading efforts to develop customised Gaussian-process solutions for recommendation systems.
Before Secondmind, I interned at Microsoft Research in Cambridge developing predictive models for Atari-style video games. The specific problem that we solved was to jointly model image sequence and reward prediction in such games (which had been unresolved until then).
I completed my PhD in artificial intelligence at the Max Planck Institutes for Intelligent Systems and Biological Cybernetics in Tuebingen, Germany. My educational background is computer science with focus on artificial intelligence (eq. BSc+MSc) but I also obtained a degree in biology with focus on neuroscience (eq. BSc+MSc), both from the University of Wuerzburg, Germany.
See Google Scholar for my publications.