Abstract
Complex tech, bio, neuro, med, eco, and socio-econ networks have both strikingly universal shared architectural features and constraining "laws" but with extremely different domain specific details. In this lecture, the speaker will use familiar case studies to motivate a new mathematical framework for understanding these similarities and differences, emphasizing layering, dynamics, optimization, nonlinearity, learning, communications, and control, sparsity and structure, and tradeoffs between robustness, efficiency, and evolvability.
About the speaker
Prof John Doyle received his BS and MS in Electrical Engineering from Massachusetts Institute of Technology in 1977 and PhD in Mathematics from University of California at Berkeley in 1984. He joined California Institute of Technology as Visiting Assistant Professor of Electrical Engineering in 1985 and is currently the Jean-Lou Chameau Professor of Control and Dynamical Systems, Electrical Engineering and BioEngineering.
Prof Doyle’s research focuses on mathematical foundations for complex networks with applications in biology, technology, medicine, ecology, neuroscience, and multiscale physics that integrate theory from control, computation, communication, optimization, statistics (e.g. Machine Learning). His early work was on robustness of feedback control systems with applications to aerospace and process control.
Prof Doyle received numerous awards including the IEEE W.R.G. Baker Award (1991), the AACC O. Hugo Schuck Best Paper Award (1995) and the ACM Sigcomm “Test of Time” paper Award (2017). He also received the AACC Donald P. Eckman Award (1983), the IEEE Centennial Outstanding Young Engineer Award (1984), and the IEEE Control Systems Field Award (2004).
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