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Christopher K. I. Williams


  • Advisor
  • Learning in Machines & Brains




Christopher Williams is a computer scientist who works in artificial intelligence (expert systems, machine learning, robotics).

He is interested in a wide range of theoretical and practical issues in machine learning, statistical pattern recognition, probabilistic graphical models and computer vision. His current research focuses on prediction with Gaussian processes and image interpretation.


  • Winton Capital Research Prize

Relevant Publications

  • Moreno, Pol. “Overcoming Occlusion with Inverse Graphics.” In Computer Vision-ECCV 2016 Workshops Proceedings, Part III, edited by H. Gang and H. Jegou, 170–185.
  • Everingham, M. et al. “The PASCAL Visual Object Classes Challenge - a Retrospective.” International Journal of Computer Vision 111, no. 1 (2015): 98–136.
  • Rasmussen, C.E., and C.K.I. Williams. Gaussian Processes for Machine Learning. Cambridge, MA: MIT Press, 2006.

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