PhD Condensed Matter Physics 1995 - Massachusetts Institute of Technology
AB 1990 - Harvard University
Dan's research focuses on applying knowledge about biological information processing systems to building better artificial sensorimotor systems that can adapt and learn from experience. Drawing from the ways in which biological systems compute and learn, Dan and his lab look at computational neuroscience models, theoretical foundations of machine learning algorithms, as well as constructing real-time intelligent robotic systems, with an ultimate goal of making machines that better understand what we want them to do.
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EdX
Dive into the world of Robotics: Vision Intelligence and Machine Learning to understand how robots 'see' and learn from their surroundings. This course explores the design and implementation of robot vision systems that enable safe human interaction and autonomous navigation.