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Ryan P. Adams

职称:Assistant Professor of Computer Science

所属学校:Harvard University

所属院系:Computer Science

所属专业:Computer Science

联系方式:(617) 495-3311

简介

In July 2011 Ryan P. Adams was appointed as an Assistant Professor of Computer Science at the Harvard John A. Paulson 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.

职业经历

In July 2011 Ryan P. Adams was appointed as an Assistant Professor of Computer Science at the Harvard John A. Paulson 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.

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