A reception will follow the talk in the WID Atrium

Abstract: This talk will focus on progress towards a more “unified” theory for complex networks motivated by biology and technology. This unification involves several elements: hard limits on achievable robust performance (“laws”), the organizing principles that succeed or fail in achieving them (architectures and protocols), the resulting high variability data and “robust yet fragile” behavior observed in real systems (behavior, data), and the processes by which systems evolve (variation, selection, design). We will leverage a series of case studies from neuroscience, cell biology, human physiology, and technology to illustrate the implications of recent theoretical developments. Hard limits on measurement, prediction, communication, computation, decision, and control, as well as the underlying physical energy and material conversion mechanism necessary to implement these abstract process are at the heart of modern mathematical theories of systems in engineering and science (often associated with names such as Shannon, Poincare, Turing, Gödel, Bode, Wiener, Heisenberg, Carnot, et cetera). They form the foundation for rich and deep subjects that are nevertheless now introduced at the undergraduate level. Unfortunately, these subjects remain largely fragmented and incompatible, even as the tradeoffs between these limits are essential to understanding human physiology and neuroscience, and are of growing importance in building integrated and sustainable systems. Time permitting, we will give an accessible introduction to these theories, how they do and don’t relate to each other, and progress and prospects for a more integrated theory. Particular emphasis will be put on Turing’s work in honor of his 100th birthday.

Bio: John C. Doyle is the John G Braun Professor of Control and Dynamical Systems, Electrical Engineering, and BioEngineering at Caltech. He has a BS and MS in EE from MIT (1977), and a PhD in Math from UC Berkeley (1984). Current research interests are the theoretical foundations for complex networks in engineering and biology and for multiscale physics. Early work was in the mathematics of robust control, including extensions to nonlinear and networked systems. Related software projects include the Robust Control Toolbox (muTools), SOSTOOLS, SBML (Systems Biology Markup Language), and FAST (Fast AQM, Scalable TCP). Prize papers include IEEE Baker, IEEE Automatic Control Transactions Axelby (twice), and best conference papers in ACM Sigcomm and AACC American Control Conference. Individual awards include AACC Eckman, and IEEE Control Systems Field and Centennial Outstanding Young Engineer Awards. He has held national and world records and championships in various sports. He is best known for having excellent co-authors, students, friends, and colleagues.