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Learning patterns in Big data from small data using core-sets, Nov 8

Abstract: When we need to solve an optimization problem we usually use the best available algorithm/software or try to improve it. In recent years we have started exploring a different approach: instead of improving the algorithm, reduce the input data and run the existing algorithm on the reduced data to obtain the desired output much faster on a streaming input, using a manageable amount of memory, and in parallel (say, using Hadoop, cloud service, or GPUs).

A core-set for a given problem is a semantic compression of its input, in the sense that a solution for the problem with the (small) core-set as input yields an approximate solution to the problem with the original (Big) data. In this talk I will describe the core-set approach and recent algorithmic achievements for computing core-sets with performance guarantees. I will also describe applications of this magical new paradigm in Machine Learning, Robotics, Computer Vision, and Privacy. Finally, I will describe in detail iDiary: a system that turns large sensor signals collected from smart-phones into textual descriptions of the trajectories. The system features a user interface similar to Google Search that allows users to type text queries on their activities (e.g., “Where did I buy books?”) and receive textual answers based on their GPS signals.

Bio: Dan Feldman is a post-doc at MIT in the Distributed Robotics Lab, where he develops systems for handling streaming Big data from sensors, smartphones, images, and robots. He got his Ph.D. from Tel-Aviv University in 2010, under the supervision of Prof. Micha Sharir and Prof. Amos Fiat. He then was a postdoc at the Center for the Mathematics of Information at Caltech for a year and a half, where he started to reduce the gap between theoretical computational geometry and practical machine learning. He is specialized in developing software for scalable data compression, based on core-set constructions with provable guarantees. His coresets were implemented in several start-ups, banks, super markets, and internet search companies over the recent years, to name just a few. When he is not working, Dan is building robots and trains from Lego with his very own coresets, Ariel and Eleanor.