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

Eamonn Keogh
Professor, University of California - Riverside


Dr. Eamonn Keogh is a professor of computer science at the University of California Riverside, specializing in data mining. He is a highly prolific researcher, as of 2015 he is one of only three people to have at least 20 papers in each of the top data mining conferences (ACM SIGKDD, IEEE ICDM and SIAM SDM), and one of only two people to have won a best paper award in each. His work on time series algorithms and data representations have been cited over 17,000 times and used in hundreds of commercial and scientific endeavors.


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.
12:00pm–12:30pm Wednesday, 02/18/2015
Hardcore Data Science
Location: LL20 BC.
Eamonn Keogh (University of California - Riverside)
Average rating: ****.
(4.67, 6 ratings)
In this talk I will argue that, relative to other types of data (text, social networks etc), time series data is relatively underexploited, and that many opportunities are available for novel commercial applications and scientific discoveries. Read more.