Faculty

David J.C. MacKay (In Memoriam)

Professor David J.C. MacKay FRS was Regius Professor of Engineering at the University of Cambridge, and an affiliate faculty member of the Machine Learning Group. More information on his research on Bayesian probability theory and its application to inference problems and his work on sustainable energy can be found on his website.

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Richard Turner

My research lies at the interface between computer perception (which builds artificial systems for understanding images, sounds and videos), neuroscience (which tries to understand the brain) and machine-learning (which provides a theoretical framework for learning from data). The goal is to develop systems that solve important problems, drawing inspiration from the brain. For example, figuring [...]

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Carl Edward Rasmussen

I have very broad interests in probabilistic inference in machine learning, covering both unsupervised, supervised and reinforcement learning. I’m particularly interested in design and evaluation of non-parametric methods such as Gaussian processes and Dirichlet processes. Exact inference in these models is often intractable, so one needs to resort to approximation methods, such as variational techniques [...]

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Zoubin Ghahramani

I studied Computer Science and Cognitive Science at the University of Pennsylvania and obtained my PhD from MIT in 1995. From 1995 to 1998, I was a Postdoctoral Fellow at the University of Toronto, working with Geoff Hinton. I was one of the founding faculty members of the Gatsby Computational Neuroscience Unit at UCL (1998-2005) [...]

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José Miguel Hernández Lobato

Since Sep 2016, I am a University Lecturer (equivalent to US Assistant Professor) in Machine Learning at the Department of Engineering in the University of Cambridge, UK. I was before a postdoctoral fellow in the Harvard Intelligent Probabilistic Systems group at the School of Engineering and Applied Sciencies of Harvard University, working with the group [...]

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