Data Summarization and Distributed Computation
University of Warwick
The notion of summarization is to provide a compact representation of data which approximately captures its essential characteristics. If such summaries can be created, they can lead to efficient distributed algorithms which exchange summaries in order to compute a desired function.
In this talk, I’ll describe recent efforts in this direction for problems inspired by machine learning: building graphical models over evolving, distributed training examples, and solving robust regression problems over large, distributed data sets.
Graham Cormode is a Professor in Computer Science at the University of Warwick in the UK, where he works on research topics in data management, privacy and big data analysis. Previously, he was a principal member of technical staff at AT&T Labs-Research. His work has attracted over 11,000 citations in the literature and has appeared in over 90 conference papers, 40 journal papers, and been awarded 30 US Patents.
Cormode is the co-recipient of the 2017 Adams Prize for Mathematics for his work on Statistical Analysis of Big Data. He has also edited two books on applications of algorithms to different areas, and co-authored a third.