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Before joining Georgia Tech, Boots served as a post-doctoral research associate in the Robotics and State Estimation Lab in the Computer Science and Engineering Department at the University of Washington, where his research focused on statistical machine learning, artificial intelligence and robotics. In his work, Boots designs learning algorithms with strong theoretical guarantees that also achieve state-of-the-art performance in applied domains. His algorithms make use of and extend theory from kernel methods, spectral methods, and predictive state representations.
He recently received his doctorate from the Machine Learning Department in the School of Computer Science at Carnegie Mellon University. At CMU, he was a member of the SELECT Lab run by Carlos Guestrin, and his adviser was Geoff Gordon. Before attending CMU, he served as a research associate in the laboratory of Dale Purves, investigating human visual and auditory perception in the Center for Cognitive Neuroscience at Duke University. Before that, he worked as an engineer at MobileRobots Inc., developing perception and navigation systems for autonomous mobile robots. He spent his undergraduate years at Bowdoin College, where he earned a double major in computer science and analytic philosophy and competed on the track and field teams.