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

Rob Fergus
New York University and Facebook


Rob Fergus is an Associate Professor of Computer Science at the
Courant Institute of Mathematical Sciences, New York University. He is
also a Research Scientist at Facebook, working in their AI Research
Group. He received a Masters in Electrical Engineering with Prof.
Pietro Perona at Caltech, before completing a PhD with Prof. Andrew
Zisserman at the University of Oxford in 2005. Before coming to NYU,
he spent two years as a post-doc in the Computer Science and
Artificial Intelligence Lab (CSAIL) at MIT, working with Prof. William
Freeman. He has received several awards including a CVPR best paper
prize, a Sloan Fellowship & NSF Career award and the IEEE
Longuet-Higgins prize.


9:00am–5:00pm Wednesday, 10/15/2014
Hardcore Data Science
Location: 1 E14/1 E15
Ben Lorica (O'Reilly), Ted Dunning (MapR, now part of HPE), 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.
2:00pm–2:30pm Wednesday, 10/15/2014
Hardcore Data Science
Location: E14 / E15
Rob Fergus (New York University and Facebook)
Average rating: ****.
(4.20, 5 ratings)
Performance on a range of perceptual tasks, such as speech understanding and image recognition, has dramatically advanced in the last year or so, due to breakthroughs in deep neural network models. Read more.