Colloquium Details
Effective, Adaptive, and Scalable Computing for Big Graph Data
| Author: | Yang Zhou Georgia Institute of Technology |
|---|---|
| Date: | May 31, 2016 |
| Time: | 15:30 |
| Location: | 220 Deschutes |
Abstract
With continued advances in science and technology, big graph (or network) data, such as World Wide Web, social networks, academic collaboration networks, transportation networks, telecommunication networks, biological networks, and electrical networks, have grown at an astonishing rate in terms of volume, variety, and velocity. Analyzing such big graph data has huge potential to reveal hidden insights and promote innovation in business, science, and engineering domains. However, there exist a number of challenging bottlenecks in developing advanced graph analytics tools in the Big Data era. My current research efforts focus on bridging graph mining and graph processing techniques to alleviate such bottlenecks in terms of both effectiveness and efficiency.
In this talk, I will present my research on exploring, understanding, and learning big graph data from two aspects: algorithm and system. First, I will discuss several graph mining algorithms to analyze real-world heterogeneous information networks. Second, I will introduce my work on graph processing systems to speed up the execution of graph applications in data mining and machine learning. Finally, I will conclude the talk by sketching interesting future directions for big graph computing, domain driven knowledge discovery, and privacy preserving in graph mining.
More details can be found at: http://www.cc.gatech.edu/~yzhou86/
Biography
Yang Zhou is a Ph.D. candidate in the College of Computing at the Georgia Institute of Technology. His primary research interests include data mining, databases, parallel and distributed computing, security and privacy, with a focus on the development of effective and scalable algorithms, systems, and applications to address the challenges of big graphs.
He has also worked with researchers from diverse research fields, such as software engineering, storage systems, cloud computing, web services, and trust management, to build and deploy novel knowledge discovery solutions to improve domain-specific system design, data management, and data analytics in real-world settings.
He has published more than a dozen papers in top conferences and journals, such as SIGKDD, VLDB, ICDM, HPDC, SC, ISSTA, TKDD, JSAC, TWEB and DMKD. Some of his works have been included in reading lists and taught in courses at universities world wide.