In the connected century, Berkeley is leading the revolution. The data revolution is opening a new era in society, science, and human life, and Berkeley is at the forefront of this big data boom. Join Interim Dean of Data Sciences David Culler, fellow faculty, and student ambassadors for a panel exploring how data and analytics are transforming the university — and the world beyond. From technologies that are changing how science is done and its implications and ethics, to empowering research and driving social change, Berkeley is leading the way in this increasingly connected century.
Berkeley 150 Roadshow — San Francisco
Date: Tuesday, October 30, 2018
Time: 6:30 PM - 8:30 PM (Pacific Time)
Location: Grand Hyatt San Francisco, 345 Stockton Street, San Francisco, California 94108
The Berkeley 150 Roadshow is a traveling lecture series that brings UC Berkeley’s premier faculty — longtime lecturers and rising stars alike — to the extended Cal family for an evening of engaging lectures, panels, and lively Q&A.
He received his BA from UC Berkeley in 1980 and an MS and PhD from MIT in 1985 and 1989, respectively. He joined the EECS faculty in 1989; is the founding director of Intel Research, UC Berkeley; and was associate chair of the EECS Department, 2010-2012 and Chair from 2012 through June 30, 2014. He won the Okawa Prize in 2013. He is a member of the National Academy of Engineering, an ACM fellow, and an IEEE fellow. He has been named one of Scientific American's Top 50 Researchers and is the creator of one of MIT's Technology Review's 10 Technologies that Will Change the World. He was awarded the NSF Presidential Young Investigator and the Presidential Faculty Fellowship. His research addresses networks of small embedded wireless devices, planetary-scale internet services, parallel computer architecture, parallel programming languages, and high-performance communication. It includes TinyOS, Berkeley Motes, PlanetLab, Networks of Workstations (NOW), Internet services, Active Messages, Split-C, and the Threaded Abstract Machine (TAM).