Keynote Speakers

Prof.Shigang Chen,
Department of Computer & Information of Science & Engineering
University of Florida, United States

Dr. Shigang Chen (sgchen@cise.ufl.edu) is a professor with Department of Computer and Information Science and Engineering at University of Florida. He received his B.S. degree in Computer Science from University of Science and Technology of China in 1993. He received M.S. and Ph.D. degrees in Computer Science from University of Illinois at Urbana-Champaign in 1996 and 1999, respectively. After graduation, he had worked with Cisco Systems for three years before joining University of Florida in 2002. He served as CTO for Chance Media Inc. during 2012-2014. His research interests include computer networks, Internet security, wireless communications, and distributed computing. He published more than 180 peer-reviewed journal/conference papers. He received IEEE Communications Society Best Tutorial Paper Award and NSF CAREER Award. He holds 12 US patents. He was an associate editor for IEEE/ACM Transactions on Networking, IEEE Transactions on Vehicular Technology, and a number of other journals. He served in various chair positions or as technical committee member for numerous conferences. He is a Fellow of IEEE, an ACM Distinguished Member, and a Distinguished Lecturer of IEEE Communication Society.

Title: Sketching Big Network Data
Abstract: The Internet has moved into the era of big network data. It presents both opportunities and technical challenges for traffic measurement functions, which have important applications in intrusion detection, resource management, billing and capacity planning, as well as big data analytics. Due to the practical need of processing network data in high volume and at high speed, past research has strived to reduce the memory and processing overhead when measuring a large number of flows. One important thread of research in this area is based on sketches, such as the FM sketches, the LogLog sketches, and the HyperLogLog sketches. Each sketch requires multiple bits and many sketches are needed for each flow, which results in significant space overhead. In this talk, we present a new method of virtual sketches to summarize big network data into extraordinarily small size. The new method compresses big data into a space of less than 1 bit per flow. Yet it supports extraction of per-flow statistics from such a small summary with good accuracy. We also show how this virtual-sketch research can be extended along space/time/function/application dimensions.

Prof.Sen Zhang
Department of Math, Computer Science and Statistics
State University of New York College at Oneonta, NY, United States

Sen Zhang received his B.S. M.S. and Ph.D., all in Computer Science, respectively from Tianjin University (1992), South China University of Science and Technology (1994) and the New Jersey Institute of Technology (2004). He joined State University of New York, College at Oneonta in 2004 and currently is Professor of Computer Science.
Dr. Zhang's research interests include data mining and databases, and their related data, data modeling, data structures and algorithms. In particular, his work focuses on practical algorithms related to sequences, trees and graphs. The results of his research have appeared in computer science journals such as ACM Transactions on Information Systems, ACM Transactions on Algorithms, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Systems, Man, and Cybernetics and IEEE Transactions on Computers.
His conference papers have appeared in proceedings of IEEE ICDE, IEEE DCC, SIAM SDM, CPM, WADS, LCN, etc.

 


Prof. Dr. Houssain Kettani
Florida Polytechnic University, Lakeland,Florida, United States

Dr. Houssain Kettani received the Bachelor's degree in Electrical and Electronic Engineering from Eastern Mediterranean University at Famagusta, North Cyprus, in 1998, and Master’s and Doctorate degrees both in Electrical Engineering from the University of Wisconsin at Madison, Wisconsin, USA in 2000 and 2002, respectively.

Dr. Kettani served as faculty member at the University of South Alabama at Mobile, Alabama, USA in 2002-2003, Jackson State University at Jackson, Mississippi, USA in 2003-2007, Polytechnic University of Puerto Rico at San Juan, Puerto Rico, USA in 2007-2012, Fort Hays State University at Hays, Kansas, USA in 2012-2016 and Florida Polytechnic University at Lakeland, Florida, USA, since 2016.

Dr. Kettani has served as Staff Research Assistant at Los Alamos National Laboratory at Los Alamos, New Mexico, USA in summer of 2000, Visiting Research Professor at Oak Ridge National Laboratory at Oak Ridge, Tennessee in summers of 2005 to 2011, Visiting Research Professor at the Arctic Region Supercomputing Center at the University of Alaska at Fairbanks, Alaska, USA in summer of 2008 and Visiting Professor at the Joint Institute for Computational Sciences at the University of Tennessee at Knoxville, Tennessee in summer of 2010.

Dr. Kettani’s research interests include computational science and engineering, high performance computing algorithms, information retrieval, network traffic characterization, number theory, robust control and optimization, and Muslim population studies. He presented his research in over sixty refereed conference and journal publications and his work received over four hundred citations by researchers all over the world. He chaired over hundred international conferences throughout the world and successfully secured external funding in millions of dollars for research and education from US federal agencies such as NSF, DOE, DOD, and NRC.