Charalampos (Babis) Tsourakakis: "Algorithm Design for Large-Scale Datasets"

In this talk we will focus on efficient algorithm design, a central challenge posed by the big data phenomenon.In the first part of the talk we will present an efficient approach to extracting dense subgraphs from large-scale networks, 

a key primitive for many graph mining applications.

In the second part, we will focus on dynamic programming, a problem solving technique used in a plethora of important applications.  We will provide two novel techniques for optimizing dynamic programming that can handle 

cost functions not treated by other standard methods. We show an application in the context of fitting piecewise constant segments to a one-dimensional signal, an optimization problem which appears under different guises in databases and data mining.  

Dr. Charalampos Tsourakakis is currently a Postdoctoral Fellow in Harvard University. 

He received his Ph.D. from the Algorithms, Combinatorics and Optimization (ACO)

program at Carnegie Mellon University. 

He holds a Diploma in Electrical and Diploma Engineering from the National 

Technical University of Athens and a Master of Science from the Machine 

Learning Department at Carnegie Mellon University. 

He won a best paper award in IEEE Data Mining, has delivered two tutorials in 

the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 

and has designed two graph mining libraries for tera-scale graphs, 

one of which has been officially included in Windows Azure. 

His research interests include algorithmic graph theory, 

random graph theory, and graph-related applications with a focus on large-scale networks.