Edinburgh Research Explorer

Prof Christopher Williams

Personal Chair, Chair of Machine Learning

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Willingness to take Ph.D. students: Yes

Research Interests

Prof. Chris Williams is interested in a wide range of theoretical and practical issues in machine learning, statistical pattern recognition, probabilistic graphical models and computer vision. This includes theoretical foundations, the development of new models and algorithms, and applications. His main areas of research are in visual object recognition and image understanding, models for understanding time-series, unsupervised learning, and Gaussian processes.

Qualifications

1978-1982University of Cambridge
 BA Physics and Theoretical Physics, Class I
1982-1983University of Cambridge
 Part III Mathematics, Distinction
1983-1984University of Newcastle upon Tyne
 MSc Water Resources
1988-1990University of Toronto
 MSc Neural Networks/Artificial Intelligence
1990-1994University of Toronto PhD
 Thesis Title: Combining Deformable Models and Neural Networks for Handprinted Digit Recognition

Biography

Prof. Chris Williams studied Physics at Cambridge, graduating in 1982 (BA in Physics and Theoretical Physics, Class I), followed by a further year of study known as Part III Mathematics (Distinction, 1983). Prof. Williams was interested in the neural networks field, but being a new field, he had difficulty finding a supervisor. He switched directions for a while,completing MSc in Water Resources at the University of Newcastle upon Tyne, then working in Lesotho, Southern Africa in low-cost sanitation. In 1988 he returned to academia, studying neural networks and artificial intelligence with Geoff Hinton at the University of Toronto (MSc 1990, PhD 1994). In September 1994, Prof. Williams moved to Aston University as a Research Fellow and was made a Lecturer in August 1995. Prof. Williams moved to the Department of Artificial Inteligence at the University of Edinburgh in July 1998, was promoted to Reader in the School of Informatics in October 2000, and Professor of Machine Learning in October 2005.

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