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Make Data Work
Oct 15–17, 2014 • New York, NY
Brian Whitman

Brian Whitman
Principal Scientist, Spotify

Website | @bwhitman

Brian is recognized as a leading scientist in the area of music and text retrieval and natural language processing.

He received his doctorate from MIT’s Media Lab in 2005 in Barry Vercoe’s Machine Listening group and a masters degree in computer science from Columbia University’s Natural Language Processing Group. Brian’s research focuses on the cultural analysis of music through large scale data mining and machine learning.
Brian recorded and performed as Blitter until he co-founded The Echo Nest, a Spotify subsidiary, and he currently works on large scale automated music synthesis.

Sessions

9:00am–5:00pm Wednesday, 10/15/2014
SOLD OUT
Hardcore Data Science
Location: 1 E14/1 E15
Ben Lorica (O'Reilly Media), Ted Dunning (MapR), Tim Kraska (Brown University), Alice Zheng (Amazon), Anna Gilbert (University of Michigan), Jon Kleinberg (Cornell University), Kira Radinsky (eBay | Technion), Rob Fergus (New York University and Facebook), Ben Recht (University of California, Berkeley), Brian Whitman (Spotify), Hanna Wallach (Microsoft Research NYC & University of Massachusetts Amherst), Dafna Shahaf (The Hebrew University of Jerusalem)
Average rating: ****.
(4.27, 15 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.
3:30pm–4:00pm Wednesday, 10/15/2014
Hardcore Data Science
Location: E14 / E15
Brian Whitman (Spotify)
Average rating: ****.
(4.75, 4 ratings)
As the dominant means of listening moves to on-demand streaming on services such as Spotify, we're now merging these machine learning and knowledge based approaches to music understanding with unprecedented amounts of user activity data to truly unlock the meaning of music taste and preference at a large scale. Read more.