Ryan P. Adams

Ryan P. Adams

  • Assistant Professor of Computer Science

Profile

In July 2011 Ryan P. Adams was appointed as an Assistant Professor of Computer Science at the Harvard School of Engineering and Applied Sciences. Previously, he was a CIFAR Junior Research Fellow at the University of Toronto.

His research focuses on machine learning and computational statistics, but he is broadly interested in questions related to artificial intelligence, computational neuroscience, machine vision, and Bayesian nonparametrics.

Adams leads the HIPS (Harvard Intelligent Probabilistic Systems) group, dedicated to building intelligent algorithms. What makes a system intelligent? The HIPS philosophy is that "intelligence" means making decisions under uncertainty, adapting to experience, and discovering structure in high-dimensional noisy data.

The unifying theme for research in these areas is developing new approaches to statistical inference: uncovering the coherent structure that we cannot directly observe and using it for exploration and to make decisions or predictions. Ryan and his team develop new models for data, new tools for performing inference, and new computational structures for representing knowledge and uncertainty.

Contact Information

Office:233 Maxwell Dworkin
Email:rpa@seas.harvard.edu
Office Phone:(617) 495-3311
Assistant:Ann Marie King
Assistant Office:Maxwell Dworkin 114
Assistant Phone:617-496-1447

Primary Teaching Area

Computer Science

Positions & Employment

University of Toronto, Department of Computer Science

  • 2009-2011: Canadian Institute for Advanced Research Junior Fellow

University of Cambridge, Cavendish Laboratory (Department of Physics)

  • 2004-2009: Ph.D. Candidate, Gates Cambridge Scholar

Massachusetts Institute of Technology, CSAIL

  • 2002-2004: Undergraduate Researcher

Honors

  • Best Paper, Thirteenth International Conference on Artificial Intelligence and Statistics(with Hanna Wallach & Zoubin Ghahramani), 2010
  • Honorable Mention, International Society for Bayesian Analysis Leonard J. Savage Award for Outstanding Dissertation in Bayesian Theory and Methods, 2010
  • Honorable Mention, Best Paper, Twenty-Sixth International Conference on Machine Learning (with Zoubin Ghahramani), 2009
  • Honorable Mention, Best Student Paper, Twenty-Sixth International Conference on Machine Learning (with Iain Murray & David J.C. MacKay), 2009