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

Maya Gupta
Research and Development Manager, Google


Gupta runs an R&D group at Google Research focused on designing efficient and transparent statistical learning algorithms. From 2003-2012, she was a professor of electrical engineering at the Univ. of Washington. Gupta received the PECASE award in 2007 from Pres. George Bush, and the ONR YIP award in 2007. She completed the PhD from Stanford EE in 2003 as an NSF Fellow, working with Bob Gray and Richard Olshen. Gupta holds a BS EE and BA Econ at Rice University, 1997. Gupta was a color image processing researcher from 2000-2003 at Ricoh’s California Research Center, and has also worked for AT&T Labs, Microsoft, NATO, and HP, and is the Founder and CEO of Artifact Puzzles.


9:00am–5:00pm Wednesday, 02/18/2015
Hardcore Data Science
Location: LL20 BC
Ben Lorica (O'Reilly), 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.
11:00am–11:30am Wednesday, 02/18/2015
Hardcore Data Science
Location: LL20 BC.
Maya Gupta (Google)
Average rating: ****.
(4.67, 3 ratings)
What makes a large machine learning system more interpretable and robust in practice? How do we take into account engineer's prior information about signals? We'll discuss the importance of monotonicity, smoothness, semantically-meaningful inputs and outputs, and designing algorithms that are easy to debug. Read more.