Containment of Epidemic Spreading Processes over Temporal Networks

Abstract: 
Understanding spreading processes over complex networks and designing control strategies to contain them are essential problems having applications in diverse areas including epidemiology, computer viruses, and information propagation in social networks. Although we can find in the literature several important advances toward this direction, many of them are still based on the simplistic assumption that the underlying complex networks are static. In this talk, we will overview recent developments in the analysis and control of spreading processes in temporal networks.

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We will specifically address the role of the theory positive switched linear systems in the modeling, analysis, and containment of epidemic spreading processes over temporal networks.

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Biography:  
Masaki Ogura is an Assistant Professor in the Division of Information Science at the Nara 
Institute of Science and Technology, where he is affiliated with Intelligent System Control Laboratory. He received his M.Sc. degree in Informatics from Kyoto University in 2009, and his Ph.D. in Mathematics from Texas Tech University in 2014. From 2014 to 2017, he was a Postdoctoral Researcher at the University of Pennsylvania. His research interests include network science, dynamical systems, and stochastic processes with applications in spreading processes over temporal networks and bet-hedging mechanisms in biological populations. 

Research Areas:

Robotics and Control