Presented By O’Reilly and Intel AI
Put AI to Work
April 29-30, 2018: Training
April 30-May 2, 2018: Tutorials & Conference
New York, NY
Tim Kraska

Tim Kraska
Professor, MIT

Website

Tim Kraska is an associate professor of electrical engineering and computer science in MIT’s Computer Science and Artificial Intelligence Laboratory and codirector of the Data System and AI Lab at MIT (DSAIL@CSAIL). His research focuses on building systems for machine learning and using machine learning for systems. Previously, Tim was an assistant professor at Brown, spent time at Google Brain, and was a postdoc in the AMPLab at UC Berkeley after his PhD at ETH Zurich. Tim’s a 2017 Alfred P. Sloan Research Fellow in computer science and received several awards including the 2018 VLDB Early Career Research Contribution Award, the 2017 VMware Systems Research Award, an NSF CAREER Award, as well as several best paper and demo awards at VLDB and ICDE.

Sessions

8:50am–9:00am Wednesday, May 2, 2018
Machine learning just ate algorithms in one large bite
Location: Grand Ballroom
Tim Kraska (MIT)
Average rating: ***..
(3.00, 1 rating)
Recent results show that machine learning has the potential to significantly alter the way basic data structures and algorithms are implemented and the performance they can provide. Tim Kraska explains the basic intuition behind learned data structures and outlines the potential consequences of this technology for industry. Read more.
11:55am–12:35pm Wednesday, May 2, 2018
Learned index structures
Implementing AI, Interacting with AI, Models and Methods
 Location: Grand Ballroom West
Tim Kraska (MIT)
Average rating: *****
(5.00, 2 ratings)
Tim Kraska explains how fundamental data structures can be enhanced using machine learning with wide-reaching implications even beyond indexes, arguing that all existing index structures can be replaced with other types of models, including deep learning models (i.e., learned indexes). Read more.