Bio:
Anura P. Jayasumana is a Professor in Electrical & Computer Engineering at Colorado State University where he holds a joint appointment in Computer Science. He is the Director of the Information Science and Technology Center (ISTeC) at CSU, a university wide organization for promoting research, teaching and service in information sciences and technologies. He received a Ph.D. and M.S. in Electrical Engineering from Michigan State University and B.Sc. in Electronic and Telecommunications Engineering with First Class Honors from University of Moratuwa, Sri Lanka. His current research interests include mining knowledge networks for radicalization detection, Internet of Things, machine learning techniques for graphs, and synthetic data generation for machine learning.. His research has been funded by DARPA, NSF, DoJ/NIJ, and industry. He served as a Distinguished Lecturer of the IEEE Communications Society (2014-17), and is currently an ACM Distinguished Speaker. He has served extensively as a consultant to companies ranging from startups to Fortune 100.
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...
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Machine Learning and Networks - Challenges, Solutions and Tradeoffs
We consider the intersection of networks and machine learning in two contexts. In the first, the data of interest...
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Machine Learning and Synthetic Data - Potential and Pitfalls
Machine Learning (ML) models have become indispensable for solving complex problems. However, they require sufficient volumes of representative training data to be...
- 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...To request a tour with this speaker, please complete this online form.
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- On Sampling and Reconstruction of Large-Scale Networks using Graph Geodesics, Matrix Completion and Machine Learning