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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Cyber-physical systems (CPS) constitute a new generation of networked embedded systems that interweave computation, communication, and control to facilitate our interaction with the physical world. Consequently, the modeling, analysis and optimization of CPS architectures cannot be done in isolation. In this talk, we will draw inspiration from complex biological systems to define new theoretical foundations and master the spatio-temporal complexity introduced by interactive sensing, computation, communication, and control.
From microbial communities and human physiology to social and biological/neural networks, complex interdependent systems display multi-scale spatio-temporal patterns that are frequently classified as non-linear, non-Gaussian, and/or fractal structures. Drawing on observed causality of biological systems, we describe a new mathematical strategy for constructing compact yet accurate models that can capture the non-linear, non-Gaussian, and/or fractal structure through a minimum number of parameters while preserving a high degree of modeling fidelity and prediction accuracy. The benefits of this mathematical modeling are tested in the context of a CPS approach to brain-machine interface for decoding human intention and describing muscle dynamics through a set of fractional order differential equations.
Aiming at understanding and harnessing the complexity of biological systems, we develop a statistical physics inspired framework for analyzing complex collective systems. More precisely, we describe the dynamics of a collective group of agents moving and interacting in a three-dimensional space through a free-energy landscape. Based on the energy landscape, we quantify missing information, emergence, self-organization, and complexity for a collective motion. Lastly, we exploit this energy model to describe and analyze the drug-drug interaction networks. Using a complex network science approach, we uncover functional drug categories along with the intricate relationships between them. Out of the 1141 drugs from the DrugBank 4.1 database, 85% of our analytical predictions are confirmed against current state of knowledge. This analysis can be used to decode unaccounted interactions and missing pharmacological properties that can lead to drug repositioning. Motivated by the intricate geometry of complex networks, I will conclude by summarizing our work on quantifying the multi-fractal properties of brain networks and fractality implications on solving the community detection problem.
Paul Bogdan is an assistant professor in the Ming Hsieh Department of Electrical Engineering at University of Southern California. He received his Ph.D. degree in Electrical & Computer Engineering at Carnegie Mellon University. His work has been recognized with a number of honors and distinctions, including the 2017 Defense Advanced Research Projects Agency (DARPA) Young Faculty Award, 2017 Okawa Foundation Award, 2015 National Science Foundation CAREER Award, 2012 A.G. Jordan Award from the Electrical and Computer Engineering Department, Carnegie Mellon University for outstanding Ph.D. thesis and service, the 2012 Best Paper Award from the Networks-on-Chip Symposium (NOCS), the 2012 D.O. Pederson Best Paper Award from IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, the 2012 Best Paper Award from the International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS), the 2013 Best Paper Award from the 18th Asia and South Pacific Design Automation Conference, and the 2009 Roberto Rocca Ph.D. Fellowship. His research interests include the theoretical foundations of cyber-physical systems, control of complex time varying interdependent networks, modeling and analysis of biological systems and swarms, new control algorithms for dynamical systems exhibiting multi-fractal characteristics, modeling biological/molecular communication, development of fractal mean field games to model and analyze biological, social and technological system-of-systems, performance analysis and design methodologies for manycore systems.
