Field of Work
In the field of machine learning, research is carried out into the principles of learning and knowledge extraction from data. Even now, the relevant algorithms already have a crucial role to play in the IT sector: Google PageRank (sorting of the links offered), Amazon’s product recommendations, all language recognition software (e.g. Siri), automatic face recognition in Picasa – all of these are essentially based on the theory of machine learning. The main research focus interests pursued by Prof. Toussaint in this area are Bayesian methods, reinforcement learning and active learning.
In the area of artificial intelligence, Prof. Toussaint’s expertise relates in particular to planning and decision-making algorithms which explicitly allow for the uncertainty of (learned) knowledge (probabilistic inference methods).
Focus areas in the field of robotics are autonomous learning systems that explore their environment independently and are able to use the knowledge collected to manipulate this environment (reinforcement and active learning as applied to robotics). Other research topics relate to trajectory optimization and stochastic regulation theory (again using probabilistic interference methods).
Marc Toussaint, born in Wertheim/ Main in 1974, studied mathematics and physics in Cologne before obtaining his doctorate at the Ruhr-University Bochum in the field of neuroinformatics in 2003. After a research stay in Edinburgh and at the Honda Research Institute, he moved to the Technical University of Berlin in 2007 to join an Emmy Noether program. He held a junior professorship at the Free University of Berlin in 2010 and became head of the Chair of Machine Learning and Robotics at the Institute of Parallel and Distributed Systems, University of Stuttgart in December 2012.
Prof. Toussaint coordinates a DFG Priority Program in the field of autonomous learning.