Anna Gilbert received an S.B. degree from the University of Chicago and a
Ph.D. from Princeton University, both in mathematics. In 1997, she was a
postdoctoral fellow at Yale University and AT&T Labs-Research. From 1998 to
2004, she was a member of technical staff at AT&T Labs-Research in Florham
Park, NJ. Since then she has been with the Department of Mathematics at the
University of Michigan, where she is now a Professor. She has received
several awards, including a Sloan Research Fellowship (2006), an NSF CAREER award (2006), the National Academy of Sciences Award for Initiatives in Research (2008), the Association of Computing Machinery (ACM) Douglas Engelbart Best Paper award (2008), the EURASIP Signal Processing Best Paper award (2010), a National Academy of Sciences Kavli Fellow (2012), and the SIAM Ralph E. Kleinman Prize (2013).
Her research interests include analysis, probability, networking, and
algorithms. She is especially interested in randomized algorithms with
applications to harmonic analysis, signal and image processing,
networking, and massive datasets.
9:00am–5:00pm Wednesday, 10/15/2014
Hardcore Data Science
Location: 1 E14/1 E15
Ben Lorica (O'Reilly Media),
Ted Dunning (MapR),
Tim Kraska (Brown University),
Alice Zheng (1977),
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)
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...
11:00am–11:45am Wednesday, 10/15/2014
Hardcore Data Science
Location: E14 / E15
In many scientific applications, there are many data we could collect but if we chose wisely or chose a summary of that data, we could answer questions of interest more efficiently. This talk will address how to acquire the data in a different way, in summarized or compressed measurements, knowing that we’re going to extract information from the data later.
11:50am–12:30pm Thursday, 10/16/2014
Location: Table E
Analyzing data is only half the battle. Anna focuses on how to collect more relevant, useful data and how to process it more efficiently. Stop by and discuss streaming/sketching algorithms for big data, statistical algorithms for large data analysis, and compressive sampling and sensing of large data.