I’m a research scientist and former professor for machine learning and now a team lead at DeepMind. My core scientific interest are intelligent machines, that are able to autonomously learn new things from scratch. I’m in particular interested in neural networks - a sort of mathematical model of the brain - and their ability to store and generalize information. This fascination goes back to my master thesis about supervised learning algorithms (‘Rprop’, 1992).

I have always been working at the boundary of new machine learning methods and their application to novel challenges: neural forecasting systems for financial trading and sales rate prediction (‘George’, 1994; ‘Bild-Zeitung’, 1998 - 2008), self-learning agents that control self-driving cars (at Stanford, 2006) or reading thoughts and even controlling brain activity ('BrainLinks BrainTools', 2011 - 2019). Brainstormers, our robotic soccer team, was a 5 times winner of the RoboCup World Championship and one of the first teams to use reinforcement learning (RL) as their core method (1998 - 2008). The data-efficient reinforcement learning algorithms Neural Fitted Q Iteration (NFQ, 2005; NFQCA, 2011) and Deep Fitted Q (DFQ, 2010) laid the ground for many methods in current Artificial Intelligence (AI) research.

I have followed my interests in various roles: as a game programmer and author for the ZX81 and ZX Spectrum (1981-1986), a computer science professor at the universities of Dortmund, Osnabrück and Freiburg (2002-2015), a Co-Founder of one of the first startups in modern AI (Cognit - Lab for learning machines, 2010-2015). In 2015, I joined DeepMind as a research scientist and team lead of the Controls Team.