Professor  Anura Jayasumana

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.

• 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...
• 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...

• 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...