Website | @kiraradinsky
Dr. Radinsky is the CTO and cofounder of SalesPredict, a sales technology company, where she is pioneering artificial intelligence based, predictive analytics solutions that transform the way companies do business.
Dr. Kira Radinsky is one of the up and coming voices in the data science community, pioneering the field of Web Dynamics and Temporal Information Retrieval. Her work focuses on the intersection of predictive data mining and the construction of algorithms that leverage web-found information and external dynamics to predict future events. A graduate of the Technion-Israel Institute of Technology, Dr. Radinsky gained international recognition for her work there and at Microsoft Research where she developed predictive algorithms that recognized the early warning of globally impactful events, (e.g. riots or diseases.)
In 2013, Kira Radinsky was named to the prestigious “35 Young Innovators Under 35”, as chosen by the MIT Technology Review.
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)
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...
1:30pm–2:00pm Wednesday, 10/15/2014
Hardcore Data Science
Location: E14 / E15
What if you don't have enough data and still want to make predictions? Small data brings a completely different set of problems than big data. Instead of dealing with scale and efficiency, the game here is to draw statistical significant results from very few noisy examples.