Decoding the Laws of Emergence and Self-Organization in Complex Collectives: Lessons Learned from Biological Systems

ECE Seminar: Decoding the Laws of Emergence and Self-Organization in Complex Collectives: Lessons Learned from Biological Systems


Starts at: February 1, 2018 4:30 PM

Ends at: 6:00 PM

Location: WEH 5421

Speaker: Dr. Paul Bogdan

Affiliation: University of Southern California

Refreshments provided: Yes

Link to Abstract

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Abstract
Cyber-physical systems (CPS) interweave computation, communication, and control to facilitate our interaction with the physical world. Drawing inspiration from biological systems, we define new theoretical foundations and master the spatio-temporal complexity of CPS in order to model, analyze, and optimize their operations. From microbial communities to neural connections and social networks, complex interdependent systems display multi-scale spatio-temporal patterns. We propose a new mathematical strategy for constructing compact yet accurate models of CPS that can capture their 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 cyber-physical systems approach to brain-machine interface for decoding human intention and describing muscle dynamics.

Harnessing the complexity of biological systems, we also discuss a statistical physics inspired framework for analyzing complex collectives. 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 the missing information, emergence, self-organization, and complexity for a collective motion. Further, we exploit this energy model to describe and analyze the drug-drug interaction networks. 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 enables us to identify unaccounted interactions and solve the drug-repositioning problem.

Despite significant research, we still face significant challenges concerning the multi-fractal geometry of time-varying complex (weighted) networks, the role of interaction intensity, the embedding of metric spaces and the design of reliable estimation algorithms. To address these issues, we introduce a reliable multi-fractal estimation approach for quantifying the structural complexity and the heterogeneity of complex networks. For the first time, we demonstrate that (i) the weights of complex networks and their underlying metric space play a key role in dictating the existence of multi-fractal scaling and (ii) the multi-fractal scaling can be localized in both space and scales. In addition, we show that the multi-fractal geometric characterization enables the construction of a scaling-based similarity metric and the identification of community structure in human brain connectome. The detected communities are accurately aligned with the brain connectivity patterns. This framework has no constraint on the target network and can be leveraged as a basis for both structural and dynamic analysis of networks in a wide spectrum of applications.

Bio
Paul Bogdan is 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 research interests include the theoretical foundations of cyber-physical systems, the control of complex time-varying interdependent networks, the modeling and analysis of biological systems and swarms, new control algorithms for dynamical systems exhibiting multi-fractal characteristics, modeling biological /molecular communication, the development of fractal mean field games to model and analyze biological, social and technological system-of-systems, performance analysis and design methodologies for many core systems.

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 (NSF) CAREER Award, the 2013 Best Paper Award from the 18th Asia and South Pacific Design Automation Conference, the 2012 A.G. Jordan Award from Carnegie Mellon University for an 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), and the 2009 Roberto Rocca Ph.D. Fellowship.