Complex Systems Summer School 2011-Faculty
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|Complex Systems Summer School 2011|
David Krakauer Evolution Module Leader and Program Director. My research is concerned with the evolutionary history of information processing mechanisms in biology and culture, with an emphasis on robust information transmission, signaling dynamics and their role in constructing novel, higher level features. The research spans several levels of organization finding analogous processes in genetics, cell biology, microbiology and in organismal behavior and society. At the cellular level I have been interested in molecular processes, which rely on volatile, error-prone, asynchronous, mechanisms, which can be used as a basis for decision making and patterning. I also investigate how signaling interactions at higher levels, including microbial and organismal, are used to coordinate complex life cycles and social systems, and under what conditions we observe the emergence of proto-grammars. Much of this work is motivated by the search for 'noisy-design' principles in biology and culture emerging through evolution that span hierarchical structures. In addition to general principles there is a need to provide an explicit theory of evolutionary history, a theory accounting for those incompressible regularities revealed once the regular components have been subtracted.
Research projects includes work on the molecular logic of signaling pathways, the evolution of genome organization (redundancy, multiple encoding, quantization and compression), robust communication over networks, the evolution of distributed forms of biological information processing, dynamical memory systems, the logic of transmissible regulatory networks (such as virus life cycles) and the many ways in which organisms construct their environments (niche construction). Thinking about niche constructing niches provides us with a new perspective on the major evolutionary transitions.
Many of these areas are characterized by the need to encode heritable information (genetic, epigenetic, auto-catalytic or linguistic) at distinct levels of biological organization, where selection pressures are often independent or in conflict. Furthermore, components are noisy and degrade and interactions are typically diffusively coupled. At each level I ask how information is acquired, stored, transmitted, replicated, transformed and robustly encoded. With collaborators I am engaged in projects applying insights from biological information processing to electronic, engineered systems.
The big question that many of us are asking is what will evolutionary theory look like once it has become integrated with the sciences of information, and of course, what will these sciences then look like?
Cris Moore Computation Module Leader. Cristopher Moore earned his Ph.D. at Cornell University in 1991 at the age of 23. After positions as a postdoc and Research Professor at the Santa Fe Institute, he become a faculty member at the University of New Mexico, where he is now a Professor jointly in the Computer Science and Physics and Astronomy departments. He has written over 80 papers at the boundary between computer science in physics.
Jessica Flack Robustness Module Leader. Jessica Flack is Professor at the Santa Fe Institute and Co-Director (with David Krakauer) of the Collective Social Computation Group. Her research program combines dynamical systems and computational perspectives in order to build a theory of how aggregate structure and hierarchy arise in social evolution. Primary goals are to understand the conditions and mechanisms supporting the emergence of slowly changing collective features that feed-down to influence component behavior, the role that conflict plays in this process, and the implications of multiple timescales and overlapping networks for robustness and adaptability in social evolution. Research foci include design principles for robust systems, conflict dynamics and control, the role of uncertainty reduction in the evolution of signaling systems, the implications of higher-order structures for social complexity and innovation, behavioral grammars and adaptive circuit construction. Flack approaches these issues using data on social process collected from animal society model systems, and through comparison of social dynamics with neural, immune, and developmental dynamics.
Simon DeDeo Emergence Module Leader
James O'Dwyer Emergence Module Leader. James O’Dwyer's research focus is in theoretical ecology, seeking to understand why seemingly universal patterns are found in many different ecological systems. From this research, he believes, light may be shed on practical issues such as the loss of biodiversity due to climate change. James holds a PhD in theoretical physics from Cambridge University, and his appointment as an Omidyar Fellow was preceded by postdoctoral fellowships at the University of Oregon and University of Leeds.
Jim Cruthfield Complexity Module Leader. James P. Crutchfield received his B.A. summa cum laude in Physics and Mathematics from the University of California, Santa Cruz, in 1979 and his Ph.D. in Physics there in 1983. He is currently a Professor of Physics at the University of California, Davis, where is Director of the Complexity Sciences Center---a new research and graduate program in complex systems. Prior to this he was Research Professor at the Santa Fe Institute for many years, where he ran the Dynamics of Learning Group and SFI's Network Dynamics Program. Before coming to SFI in 1997, he was a Research Physicist in the Physics Department at the University of California, Berkeley, since 1985. He has been a Visiting Research Professor at the Sloan Center for Theoretical Neurobiology, University of California, San Francisco; a Post-doctoral Fellow of the Miller Institute for Basic Research in Science at UCB; a UCB Physics Department IBM Post-Doctoral Fellow in Condensed Matter Physics; a Distinguished Visiting Research Professor of the Beckman Institute at the University of Illinois, Urbana-Champaign; and a Bernard Osher Fellow at the San Francisco Exploratorium. Over the last three decades Prof. Crutchfield has worked in the areas of nonlinear dynamics, solid-state physics, astrophysics, fluid mechanics, critical phenomena and phase transitions, chaos, and pattern formation. His current research interests center on computational mechanics, the physics of complexity, statistical inference for nonlinear processes, genetic algorithms, evolutionary theory, machine learning, quantum dynamics, and distributed intelligence. He has published over 100 papers in these areas.
Elizabeth Bradley Concepts: Nonlinearity Module Leader
Mark Newman Networks Module Leader
Dan Rockmore Machine Learning Module Leader