- When: Thursday, May 23, 2019 from 01:30 PM to 02:30 PM
- Speakers: Howie Huang, George Washington University
- Location: Nguyen Engineering 4201
- Export to iCal
Today we live in a highly connected world. Our society, commerce, and national security all depend on a myriad of networks that range from transportation networks to power grids, from biological networks to personalized healthcare, from wearable devices to autonomous vehicles, and from computer networks to social networks. In Graph Computing Lab at the George Washington University, we are developing novel graph-based machine learning algorithms and systems to manage big data generated by these networks, to understand the contextual and causal relationships within entities and events, and to deliver actionable knowledge to stakeholders in real time. In this talk, I will share our experiences in designing and developing high-performance graph systems, and discuss our techniques for addressing the algorithmic, computational, and I/O challenges in graph computing. In addition, I will present our ongoing work on utilizing these graph systems for understanding and analyzing complex network data for cyber threat hunting.
Dr. Howie Huang is a Full Professor in Department of Electrical and Computer Engineering, with a courtesy appointment in Department of Computer Science, and Director of the Graph Computing Lab at the George Washington University. Motivated by the needs of big data and cybersecurity applications, he works at the intersection of algorithms, computer architecture and systems, with focus on developing high-performance computing and machine learning techniques tailored for large-scale graph datasets. Dr. Huang is a recipient of the National Science Foundation CAREER Award, NVIDIA Academic Partnership Award, Comcast Technology Research and Development Fund Award, and IBM Real Time Innovation Faculty Award. His work on big graph traversal has ranked highly on both the Graph500 and Green Graph500 benchmarks, which measure the performance and energy efficiency of the most powerful data-intensive supercomputers in the world. His research won a Champion Award and a Student Innovation Award at the 2018 Graph Challenge of IEEE High-Performance Extreme Computing conference, two awards (Finalist and Honorable Mention) at the DARPA Graph Challenge 2017, the Best Paper Award Nomination at NVMSA'17, the ACM Undergraduate Student Research Competition Winner at SC'12, a Best Student Paper Finalist at SC'11, the Best Poster Award at PACT'11, and a High-Performance Storage Challenge Finalist at SC'09. Dr. Huang is an Associate Editor for IEEE Transactions on Parallel and Distributed Systems and IEEE Transactions on Cloud Computing. He was the Associate Chair and Interim Chair in Department of Electrical and Computer Engineering in 2015-2016. He received a PhD in Computer Science from the University of Virginia.Posted 3 months ago