Computer scientist Ryan Adams wins DARPA Young Faculty Award

Computer scientist Ryan Adams will use his DARPA Young Faculty Awa

Computer scientist Ryan Adams will use his DARPA Young Faculty Award to explore new ways to make probabilistic models and the manipulation of these models work on modern computational architecture. (Photo by Eliza Grinnell, SEAS Communications.)

$300,000 grant will support work on building new computational tools that exploit statistical inference
Ryan Adams, Assistant Professor of Computer Science at the Harvard School of Engineering and Applied Sciences (SEAS), has won a Defense Advanced Research Project Agency (DARPA) Young Faculty Award.

Adams will receive $300,000 to support his project titled "Developing New Methods of Multi-Core Statistic Inference Towards Rapid Data Fusion and Information Extraction."

The grant will be focused on pursuing research towards large-scale inference using Markov chain Monte Carlo methods. Many of the most powerful modern techniques for data analysis and machine learning rely on probabilistic models, and the manipulation of these models often presents a significant computational challenge.

Adams’ group is looking for new ways to make these kinds of algorithms work on modern computational architecture, which favors multiple weakly-coupled processors rather than a single fast CPU.

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"This is an exciting research direction, as significant progress in these techniques impacts not only computer science, but also many other fields such as physics, statistics, and biology," says Adams. "We hope to couple innovative new mathematical foundations with engineering efforts to build practical and widely useful systems."

Adams joined Harvard in July 2011; he runs the Harvard Intelligent Probabilistic Systems group, which is dedicated to building intelligent algorithms.

Previously, he was a CIFAR Junior Research Fellow at the University of Toronto. He earned his Ph.D. in Physics from the University of Cambridge and B.S. in Electrical Engineering and Computer Science from MIT.

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.