Professor Carl Rasmussen

Professor of Information Engineering at Department of Engineering, University of Cambridge

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Professor of Information Engineering, Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge

Professor Carl Rasmussen works within the Machine Learning Group, where his research focuses on probabilistic inference in machine learning, covering both unsupervised and supervised learning. He is also interested in reinforcement learning and how it can be improved using a model-based approach with probabilistic models.

His other area of interest is in the design and evaluation of non-parametric methods for learning such as Gaussian processes and Dirichlet processes.

Professor Rasmussen has published literature on Gaussian Processes of Machine Learning; which are principled, practical, probabilistic approaches to learning in kernel machines. The book describes Gaussian process approaches to regression and classification, and discusses methods for hyperparameter tuning and model selection. His other literature discusses the use of Gaussian processes in the prediction of atmospheric Carbon Dioxide concentrations.

In addition to his role at the University of Cambridge, Professor Rasmussen is also an Adjunct Research Scientist at the Max Planck Institute for Biological Cybernetics in Germany, as well as a Fellow of Darwin College, Cambridge.