Princeton University

School of Engineering & Applied Science

Computational Methods for Complex Systems: From Brain to Power Networks

Somayeh Sojoudi
E-Quad, B205
Monday, March 23, 2015 - 4:30pm

In this talk, we develop mathematical techniques for the analysis, modeling and control of complex networks such as brain and electrical power networks. Two main questions to be addressed are as follows: 1) how to learn a model from data? 2) how to deal with the nonlinearity of a system? Our results will be presented in the contexts of brain and power systems separately.
Brain networks: The study of brain networks enables us to address fundamental problems ranging from understanding how the brain processes speech to localizing seizure-generating areas in epilepsy patients. In this talk, we explore three types of brain networks, namely structural, functional, and effective connectivity. We offer mathematical results and empirical observations through three case studies. First, we analyze brain networks under various speech conditions using ECoG technology.  Second, we discuss the communication networks in the hippocampus and entorhinal cortex. Third, we study the modeling and analysis of the brain functional connectivity using functional MRI technology. 
Power networks: The real-time operation of a power network depends on a nonlinear optimization problem, named optimal power flow (OPF), which is solved every 5-15 minutes in practice. According to recent Federal Energy Regulatory Commission's studies, 5% improvement in the solution of OPF reduces the annual cost by more than one billion dollars. In this talk, we propose a convexification technique for the long-standing OPF problem, followed by theoretical results and simulations on real data. 

Somayeh Sojoudi is currently an assistant research scientist at the New York University (NYU) School of Medicine, where she collaborates with a team of neuroscientists, neurologists and neurosurgeons at the comprehensive epilepsy center and the neuroscience institute. Her current research is focused on the study of the brain and the development of efficient mathematical tools and techniques for neuroscience and clinical neurology applications. Before joining NYU, she was a PhD student in Control and Dynamical Systems at the California Institute of Technology. Her interdisciplinary research on complex systems span control systems, optimization theory, power systems, communication networks, and health. She is a co-author of a best student paper award finalist for the 53rd IEEE Conference on Decision and Control 2014. She is recipient of a postgraduate scholarship from the Natural Sciences and Engineering Research Council of Canada and had been awarded the 2008 F. A. Gerard Prize.