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Sensor and Social Networks: A Case for Topological Data Analysis
May 4, 2015 @ 2:00 pm - 3:00 pm
Prof. Hamid Krim,North Carolina State University, US
Network science has permeated all areas of applications of data science, including dimension reduction, to sensor networks to social networks. While graph-based methods have long been as a technique of choice to model the network structure, the pair-wise relationship between nodes/agents/sensors has been recognized as a limitation in many practical cases. The so-called topological data analysis (TDA) has over the last few years, demonstrated that many timely, important and relevant questions in network science can be effectively addressed. These include rapid detection and localization of failures in sensor networks, and core-periphery decomposition of social networks (for community detection). We discuss some efficient, distributed and fast techniques which not only account for the underlying homology of the network, but also offer insight in their functionality.
Hamid Krim received his degrees in Electrical Engineering. As a member of technical staff at AT&T Bell Labs, he has worked in the area of telephony and digital communication systems/subsystems. In 1992, he joined the Laboratory for Information and Decision Systems, MIT, Cambridge, MA, as a Research Scientist performing/supervising research in his area of interest. In 1998, he joined the Electrical and Computer Engineering Department at North Carolina State University, Raleigh, N.C., where he is currently Professor and directing the Vision, Information, Statistical Signal Theories and Applications (VISSTA) Laboratory. Dr. Krim’s editorial activities include: Editorial Board Member, IEEE Transactions on Signal Processing (2002-2004); IEEE Signal Processing Magazine (2014). Dr. Krim is an IEEE Fellow and his research interests are in statistical signal processing and mathematical modeling with a keen emphasis on applications. He has been particularly interested in introducing geometric and topological tools to statistical signal processing problems and applications. His research has primarily centered on estimation theoretic problems and modeling.
Karim Seghouane,IEEE Signal Processing Society – Victorian Chapter Chair firstname.lastname@example.org