BIDS Senior Fellow Bin Yu will be one of this year's featured speakers at the Berkeley AI Summit at UC Berkeley's Haas School of Business. This year's event is entitled "Bridging the gap between AI research and business applications" and will bring together diverse perspectives on AI from multiple corners of campus and industry, to help all people start thinking about AI in a more comprehensive, nuanced, and thoughtful way, and to prepare effective and responsible future leaders. The event is intended to be a useful networking platform for interaction between students and professionals, and will consist of multiple speakers with diverse backgrounds in AI/ML, as well as opportunities for attendees to speak with representatives from AI-based companies from startups to major tech firms.
Bin Yu is Chancellor’s Professor in the Departments of Statistics and of Electrical Engineering & Computer Science at the University of California at Berkeley and a former Chair of Statistics at Berkeley. She is founding co-director of the Microsoft Joint Lab at Peking University on Statistics and Information Technology. Her group at Berkeley is engaged in interdisciplinary research with scientists from genomics, neuroscience, and medicine. In order to solve data problems in these domain areas, her group employs quantitative critical thinking and develops statistical and machine learning algorithms and theory. She has published more than 100 scientific papers in premier journals in statistics, machine learning, information theory, signal processing, remote sensing, neuroscience, genomics, and networks.
She is a member of the U.S. National Academy of Sciences and fellow of the American Academy of Arts and Sciences. She was a Guggenheim Fellow in 2006, an invited speaker at ICIAM in 2011, the Tukey Memorial Lecturer of the Bernoulli Society in 2012, and an invited speaker at the Rietz Lecture of Institute of Mathematical Statistics (IMS) in 2016. She was IMS president in 2013–2014, and she is a fellow of IMS, ASA, AAAS, and IEEE. She has served or is serving on leadership committees of NAS-BMSA, SAMSI, IPAM, and ICERM and on editorial boards for the Journal of Machine Learning, Annals of Statistics, and Annual Review of Statistics.