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Title: CS Colloquium: Qin Zhang, Assistant Professor, School of Informatics, Computing and Engineering
Sharing: Public
Start Time: Friday September 14, 2018 03:00 PM
End Time: Friday September 14, 2018 04:00 PM
Location: Luddy Hall, Rm. 1106, Dorsey Learning Hall
Contact: Amr Sabry
Free/Busy: busy
Description:

The School of Informatics, Computing, and Engineering (SICE) CS Colloquium Series

Speaker:   Qin Zhang, Assistant Professor, School of Informatics, Computing, and Engineering

Where:  Luddy Hall, Rm. 1106 (Dorsey Learning Hall)

When:  Friday, September 14, 2018, 3:00 pm

Topic:  Communication-Efficient Computation on Distributed Data: Theory and Practice

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).  

Biography: Qin Zhang is an assistant professor at the Indiana University Bloomington.  He received a B.S. degree from Fudan University and a Ph.D. from Hong Kong University of Science and Technology.  He also spent a couple of years as a post-doc 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 on distributed data, and communication/space lower bounds; as well as algorithms for fundamental problems in databases, machine learning, data mining and bioinformatics.

Poster

Contact Email: sabry@indiana.edu
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