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Biographical Sketch

 Curriculum Vitae: HTML PDF

Jim 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 he is helping to start up its new Center for Computational Science and Engineering. Until recently he was Research Professor at the Santa Fe Institute, where he ran the Dynamics of Learning Group, and Adjunct Professor of Physics in the Physics Department, University of New Mexico, Albuquerque. Before coming to SFI in 1997, he was a Research Physicist in the Physics Department at the University of California, Berkeley, since 1985. He also 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, distributed intelligence, and quantum computation. He has published over 100 papers in these areas; many are available from his website:

| Computational Mechanics   | Dynamics of Learning
| Evolving Cellular Automata | Evolutionary Dynamics

The unifying theme of my research is patterns—what they are, how nature produces them, and how we discover new ones. The origins of this interest date back to the 1970s, when the advent of powerful and interactive computers stimulated much work on nonlinear dynamics—deterministic chaos and bifurcations between distinct behaviors. This early work raised a number of questions on how the properties of nonlinear systems bear on the foundations of statistical mechanics, including the existence of nonequilibrium states and how one distinguishes molecular chaos—required to derive macroscopic properties from microscopic dynamics—from the mechanisms of deterministic chaos.

Progress during the 1980s in analyzing increasingly more complex nonlinear systems eventually showed that these foundational questions were special cases of broader issues: How is it that nature spontaneously generates macroscopic order and structure? What mechanisms support the production of structure? How does nature balance randomness and order as structure emerges? And, perhaps most important of all, what do we mean by structure, pattern, order, and regularity? Can there be a theory that allows us to measure patterns as concretely and workably as we measure randomness using thermodynamic entropy and temperature?

This focus on patterns led to an even more central question, How do we (or any agent moving through the natural world) discover patterns in the first place? I call this pattern discovery to distinguish it from pattern recognition—familiar in engineering, where one designs systems with a built-in palette of templates, and familiar in the natural sciences, where one analyzes data in terms of an hypothesized representation, such as with Fourier transforms. In these cases, a pattern is recognized when data most closely matches one of the stored templates. Pattern recognition, however, begs the question of discovery, Where do these representations come from in the first place?

Answering these questions led me to develop a generalization of statistical mechanics that explicitly defines structure and connects structure in natural systems to how they store and process information. In short, one asks, How does nature compute? The theory—unsurprisingly called computational mechanics—attempts to answer three quantitative questions (i) how much historical information does a system store, (ii) where is that information stored, and (iii) how is it processed to produce future behavior? These computational properties complement the questions we typically ask in physics: How much energy is stored, in what form is it stored, and how is it transformed over time and space?

In its approach to patterns computational mechanics uses the basic paradigm of statistical mechanics to synthesize nonlinear dynamics with information and computation theories. Over the last decade it has been used in a number domains, some well outside physics—in learning theory, evolutionary biology, and neuroscience, for example. My current research focuses on applying computational mechanics to structure in disordered materials, distributed coordination in collectives of intelligent agents, pre-biotic evolution, quantum computation, and the dynamics of learning itself.

Synergistic Activities:

  1. Scientific Director of a major NSF-funded science museum exhibit series on pattern formation and complex systems—Turbulent Landscapes: The Forces that Shape Our World at the San Francisco Exploratorium, July-December 1996. The show toured the nation's science museums since that time and, most recently, was on display at the British Museum in London.
  2. Director of SFI's Intel-sponsored Program on Network Dynamics.
  3. Director of the SFI's Dynamics of Learning Group and Computation, Dynamics, and Learning Program.
  4. With composer David Dunn, constructing the Theater of Pattern Formation, a large-scale multichannel video-audio exploration of structure and emergence in the spatial and acoustic domains. A work in progress, it has been performed in a number of venues including the 2006 California Institute of the Arts Center for Experiments in Art, Information and Technology festival and Burning Man. Target venues are sensory-immersive interative environments and theaters.
  5. Co-founder, Vice President, and Scientific Director of the Art and Science Laboratory, Santa Fe, New Mexico, a nonprofit research center that supports collaborations between artists and scientists working in the computing arts.
  6. Referee and Editorial Board member for journals in theoretical physics, mathematical biology, computer science, nonlinear mathematics, engineering, and complex systems.
  7. Member of the Information Technology and Creativity committee of the National Academies' Computer Science and Technology Board.