Prof. Ralph Schroeder

Ralph Schroeder has interests  in virtual environments, social aspects of e-Science, sociology of science and tech, and has written extensively about virtual reality technology. His current research is related to digital media and right-wing populism.  Ralph Schroeder was formerly Professor in the School of Technology Management and Economics at Chalmers University in Gothenburg (Sweden). He […]

Prof. Dino Sejdinovic

Assoc. Prof. Dino Sejdinovic is broadly interested in statistical foundations underpinning large-scale machine learning algorithms, with a particular emphasis on nonparametric and kernel methods, and the resulting expressive models. Recent research focused on methods for discovery of higher-order interactions in datasets (when weak individual causes combine in a nonlinear way to form a strong effect), […]

Prof. Jared Tanner

Prof. Tanner’s research concerns extracting models of high dimensional date which reveal of the essential information in the data.  Specific contributions include the derivation of sampling theorems in compressed sensing using techniques from stochastic geometry and the design and analysis of efficient algorithms for matrix completion which minimise over higher dimensional subspaces as the reliability of the data warrants. […]

Prof. Ulrike Tillmann

Prof. Ulrike Tillmann FRS has been at the University of Oxford since 1992. She is an algebraic topologist, known in particular for her work on Riemann surfaces and the homology of their moduli spaces. She has long standing research interest in homology stability questions. In 2011 she introduced an annual course (with Abramsky) in Computational […]

Prof. Shimon Whiteson

Assoc. Prof. Shimon Whiteson’s research focuses on artificial intelligence. His goal is to design, analyse, and evaluate the algorithms that enable computational systems to acquire and execute intelligent behaviour. He’s particularly interested in machine learning, with which computers can learn from experience, and decision-theoretic planning, with which they can reason about their goals and deduce […]

Prof. Frank Wood

Prof. Wood is an associate professor at the Department of Engineering Science, University of Oxford. Before that Dr. Wood was an assistant professor of Statistics at Columbia University and a research scientist at the Columbia Center for Computational Learning Systems. He formerly was a postdoctoral fellow of the Gatsby Computational Neuroscience Unit of the University […]

Prof. Yee Whye Teh

Prof. Yee Whye Teh’s research interests are in machine learning and computational statistics, in particular scalable learning, probabilistic models, Bayesian nonparametrics and deep learning. He is particularly interested in theoretically well-founded, but practically relevant, statistical algorithms for learning from data. He has worked on applications in genetics/genomics, text processing, recommender systems, and machine vision.