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Anura Jayasumana

Professor Anura Jayasumana

Based in Fort Collins, CO, United States

Bio:

res_apj_ACM_DL_0120.Anura Jayasumana is a Professor in Electrical & Computer Engineering at Colorado State University, where he also holds joint appointments in Computer Science and Systems Engineering Departments. Prof. Jayasumana’s areas of expertise include network and data mining, network analytics, Internet of Things, network coordinate systems, peer-to-peer networks, detection of distributed patterns, and profile detection in social network data.

He has served as the Principal Investigator/Co-PI/Project Member of numerous DARPA, NSF, AFRL, ARL, DoJ/NIJ (Department of Justice) and industry funded projects. He was also a member of the NSF Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere, and served as CASA Education & Outreach Lead at CSU. He has served extensively as a consultant to industry dealing with cutting-edge technologies, was involved with a start-up company, and has served as an expert witness.

Professor Jayasumana is a member of the ACM. He served as an IEEE Communications Society Distinguished Lecturer from 2014-201. He has an extensive record of keynote speeches, distinguished lectures, research seminars and tutorials. He received the Ph.D. and M.S. degrees in Electrical Engineering from the Michigan State University, and B.Sc. degree in Electronics & Telecommunications Engineering from the University of Moratuwa. See http://www.engr.colostate.edu/~anura for additional details.

Available Lectures

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Finding Emergent Patterns of Behaviors in Dynamic Heterogeneous Social and Behavioral Data: Experience with Violent Extremist Radicalization Trajectories
The search for individuals or entities undertaking latent or emergent behaviors has applicability in the areas of homeland security, consumer analytics, behavioral health, and cybersecurity. In...
On Sampling and Reconstruction of Large-Scale Networks using Graph Geodesics, Matrix Completion and Machine Learning
Extracting connectivity information in massive social networks is important for many applications. We present a method to extract the network topology from a small sample of geodesics...
Topology Preserving Maps: A Localization-Free Approach for 2-D and 3-D IoT Subnets
Driven by higher potency and lower cost/size of devices capable of sensing, actuating, processing and communicating, the Internet of Things and of Everything promises to dramatically...

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