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A Complex Networks Approach to Data Science: Modeling, Representation and Analysis of Interconnected Large-Scale Data Structures

Speaker: Dr. Saray Shai
University of North Carolina at Chapel Hill

February 28, 2017, 4:00 pm
Location: Troy 2018
Hosted By: Prof. Boleslaw Szymanski (x6838)


Driven by modern applications and the abundance of empirical network data, network science aims at analyzing real-world complex systems arising in the social, biological and physical sciences by abstracting them into networks (or graphs). The size and complexity of real networks has produced a deep change in the way that graphs are approached, and led to the development of new mathematical and computational tools. In this talk, I will present a data-driven network-based methodology to approach data analysis problems arising in a variety of contexts while highlighting state of the art network models including spatial, multilayer, interdependent and modular networks. I will describe different stages in the analysis of data starting from (1) network representations of urban transportation systems, then (2) inference of structural patterns in networks and phase transitions in their detectability, and finally (3) understanding the implications of structural features to dynamical processes taking place on networks. I will conclude with a discussion of the future directions of my work and its intersection with complexity theory, machine learning and statistics.


I received my Bachelors of Science degree, cum laude, in Mathematics and Computer Science (double degree) from the Israel Institute of Technology (Technion) in 2008. I then shifted my focus towards acquiring Industrial experience and worked as a Software Engineer at Diligent Technologies, an Israel startup that was acquired by IBM. I completed my PhD in Computer Science at the University of St Andrews, UK under the guidance of Simon Dobson in 2014. During my PhD I was supported by a full prize scholarship from the Scottish Informatics and Computer Science Alliance (SICSA). In 2014 I joined Peter Mucha’s group in the Department of Mathematics at the University of North Carolina at Chapel Hill as a Postdoctoral Scholar. My research has been focused on the development of mathematical and computational tools for modeling and analyzing complex systems, roughly defined as large networks of simple components with no central control that give rise to emergent complex behavior. I believe that looking at data through a “network lens” provides us with useful perspectives on diverse problems, from designing optimal transportation systems and smart cities to clustering high-dimensional data. I am constantly looking for new datasets on which I can apply my “network machinery” to solve real-world problems and to inspire the development of new methodologies.

Last updated: January 31, 2017