Abstract
Topic: Computational Bio-Social complexity science
Real-world bio-social habitats are often represented as co-evolving networks. Reasoning about such complex systems is complicated and scientifically challenging due to their size, co-evolutionary nature and multiple contagions spreading simultaneously. Examples include: human immune system, The 2019 COVID-19 pandemic, 2009 financial crisis, 2003 Northeast power blackout, global migration, information propagation over social media, societal impacts of natural and human initiated disasters and the effect of climate change. Advances in computing have fundamentally altered how such bio-social complex systems can be synthesized, analyzed and reasoned.Graphical Dynamical systems (GDS) can be used to model and represent large co-evolving bio-social habitats. The talk will describe a computational theory of GDS with the aim of developing scalable and practical decision support systems coevolving bio-social habitats. The role of AI and high performance computing will be highlighted. I will draw on our work in urban transport planning, national security and public health epidemiology to guide the discussion.
Bio
Madhav Marathe is a Distinguished Professor in Biocomplexity, the division director of the Network Systems Science and Advanced Computing Division at the Biocomplexity Institute and Initiative, and a Professor in the Department of Computer Science at the University of Virginia. His research interests are in network science, Sustainable habitats, AI, foundations of computing and high performance computing. Over the last 20 years, his division has supported federal and state authorities in their effort to respond to a number of problems arising in the context of national security, sustainability and pandemic science, including, the COVID-19 pandemic. Before joining UVA, he held positions at Virginia Tech and the Los Alamos National Laboratory. He is a Fellow of the IEEE, ACM, SIAM and AAAS.