Presented By O'Reilly and Cloudera
Make Data Work
Feb 17–20, 2015 • San Jose, CA
John Canny

John Canny
Professor, UC Berkeley

Website

John Canny (http://en.wikipedia.org/wiki/John_Canny) is a Paul and Stacy Jacobs Distinguished professor in computer science at UC Berkeley. He is an ACM dissertation award winner and a Packard Fellow. He is currently a Data Science Senior Fellow in Berkeley’s new Institute for Data Science and holds a INRIA (France) International Chair. Since 2002, he has been developing and deploying large-scale behavioral modeling systems. He designed and protyped production systems for Overstock.com, Yahoo, Ebay, and Quantcast. He currently works on several applications of data mining for human learning (MOOCs and early language learning), health and well-being, and applications in the sciences.

Sessions

9:00am–5:00pm Wednesday, 02/18/2015
Hardcore Data Science
Location: LL20 BC
Ben Lorica (O'Reilly Media), Ben Recht (University of California, Berkeley), Chris Re (Stanford University | Apple), Maya Gupta (Google), Alyosha Efros (UC Berkeley), Eamonn Keogh (University of California - Riverside), John Myles White (Facebook), Fei-Fei Li (Stanford University), Tara Sainath (Google), Michael Jordan (UC Berkeley), Anima Anandkumar (UC Irvine), John Canny (UC Berkeley), David Andrzejewski (Sumo Logic)
Average rating: ****.
(4.86, 7 ratings)
All-Day: Strata's regular data science track has great talks with real world experience from leading edge speakers. But we didn't just stop there—we added the Hardcore Data Science day to give you a chance to go even deeper. The Hardcore day will add new techniques and technologies to your data science toolbox, shared by leading data science practitioners from startups, industry, consulting... Read more.
2:00pm–2:30pm Wednesday, 02/18/2015
Hardcore Data Science
Location: LL20 BC.
John Canny (UC Berkeley)
Average rating: ***..
(3.86, 7 ratings)
How fast can machine learning (ML) and graph algorithms be? In "roofline" design, every kernel is driven toward the limits imposed by CPU, memory, network etc. "Codesign" pairs efficient algorithms with complementary hardware. These methods can lead to dramatic improvements in single node performance: BIDMach is a toolkit for machine learning that uses rooflined design and GPUs to... Read more.