Many real-world processes are composed of lots of small components that interact in simple ways at small scales to produce nontrivial large-scale effects. Probing the mechanisms that govern such systems— broadly called “complex systems”—is crucial for control, design, and intervention relevant to these processes, and networks form a fundamental representation allowing for this analysis. In this talk, I will first discuss how we can improve the mathematical and computational tools within network theory for tackling difficult statistical inference tasks and mining structural patterns in real-world network data. I will then move on to demonstrate how the tools from network theory can be used to shed light on modern societal problems in urban systems, from congestion to socioeconomic inequality.
Speaker: Mr Alec KIRKLEY
Date: 5 February 2021 (Fri)
Time: 10:00am – 11:00am
Poster: Click here
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Biography
I am a PhD candidate in the University of Michigan Physics Department, working on the theory of complex networks and statistical physics, supported by an National Defense Science and Engineering Graduate (NDSEG) Fellowship through the US Department of Defense. My research focuses on theoretical and computational modelling of complex networks, which includes developing novel and scalable algorithms, measures, and statistical methods for studying networks in isolation as well as for applications in urban and social systems. Topics of my research include statistical inference on network data, human mobility and the structure and function of urban systems, social network dynamics, and the spatial manifestation of socioeconomic inequality. (personal website: https://aleckirkley.com)