Santa Fe Institute


Research Themes

Research Projects

Interest Areas

  • Computational mechanics
  • Distributed intelligence
  • Evolutionary theory
  • Genetic algorithms
  • Machine learning
  • Nonlinear dynamics, chaos, and pattern formation
  • Nonlinear physics: Solid-state physics, astrophysics, fluid mechanics, critical phenomena and phase transitions
  • Physics of complexity
  • Quantum dynamics
  • Statistical inference for nonlinear processes

James P. Crutchfield

External Professor

Director, Complexity Sciences Center, Professor of Physics, University of California, Davis

Curriculum Vitae


Jim Crutchfield is a Professor of Physics at the University of California, Davis, where he directs a new research and graduate program at the Complexity Sciences Center. Prior to this he was Research Professor at the Santa Fe Institute for many years, where he directed 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, Uni- versity 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. He 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.

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 140 papers in these areas, most are available from his website:∼chaos.


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