Colloquium: Communication-Efficient Computation on Distributed Data: Theory and Practice
February 18, 2019 - 11:15am to 12:15pm
Indiana University at Bloomington
3-180 Keller Hall
Abstract: Through the massive use of mobile devices, data clouds, and the rise of the Internet of Things, large amounts of data have been generated, digitized, and analyzed for the benefit of society at large. As data are often collected and maintained at different sites, communication has become necessary for nearly every computational task. Moreover, decision makers naturally want to maintain a centralized view of all the data in a timely manner, which requires frequent queries on the distributed data. The communication cost, which contributes to most of the data/bandwidth usage, energy consumption and response time, becomes the bottleneck for many tasks. Today I will talk about my recent work on understanding communication-efficient distributed computation for fundamental algorithmic problems, from both the theoretical perspective (i.e., design algorithms with performance guarantees and explore the theoretical limits) and the practical perspective (i.e., make algorithms work well in real-world datasets).
Bio: Qin Zhang is an assistant professor in the Department of Computer Science at the Indiana University Bloomington. He received his Ph.D. from Hong Kong University of Science and Technology. Prior to IUB, he spent a couple of years as a postdoc at Theory Group, IBM Almaden Research Center, and Center for Massive Data Algorithmics, Aarhus University. He is interested in algorithms for big data, in particular, streaming/sketching algorithms, algorithms for distributed data, and fundamental problems in databases, machine learning, and data mining. He is a recipient of several NSF grants (including a CAREER Award) and Best Paper Award at SPAA 2017.