We are in an age when massive digital data continues to be acquired at an extraordinarily rapid rate and with high complexity and uncertainty. At the same time, the data and the actors behind are increasingly interconnected, and computing architectures continue to rapidly evolve. As a result, there is a need for fast, scalable and robust methods for analyzing massive datasets and extracting knowledge and insight. Network and matrix algorithms -- separately, and more importantly, in symbiosis -- play a crucial role in the construction of such methods. In this presentation, I will demonstrate these phenomena via examples from recent activities in my lab (the Scalable Algorithms for Data Science Lab (SCADS)) at the School of EECS at WSU.
Biographical Sketch: Dr. Assefaw Gebremedhin is currently an Assistant Professor in the School of Electrical Engineering and Computer Science at Washington State University, where he leads the Scalable Algorithms for Data Science Lab. Prior to joining WSU in Fall 2014, he was a Research Assistant Professor at Purdue University Department of Computer Science. His research interests include: combinatorial scientific computing, network science, high-performance computing, data mining and machine learning, bioinformatics, and health analytics.