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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 Distinguished Professor of Physics at the University of California, Davis, where he directs the Complexity Sciences Center. Before coming to UC Davis 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. And, 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 Scholar at the Institute for Advanced Study at the University of Amsterdam; Sloan Center for Theoretical Neurobiology, University of California, San Francisco; Physics Department at the California Institute of Technology; and Redwood Center for Theoretical Neuroscience. He has been a Postdoctoral Fellow of the Miller Institute for Basic Research in Science at UCB; a UCB Physics Department IBM Postdoctoral 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.

Research:

Over the last four 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, animal behavior, and quantum computation. He has published over 250 papers in these areas; many are available from his website: csc.ucdavis.edu/~chaos.

| 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.

PhD Recipients:

  1. James Hanson (1994, University of California, Berkeley) “Computational Mechanics of Cellular Automata”. First position: Postdoctoral fellow at Santa Fe Institute. Current position: Research Scientist, IBM Thomas J Watson Research Center, Hawthorne, NY.
  2. Karl Young (1995, University of California, Santa Cruz) “The Grammar and Statistical Mechanics of Complex Physical Systems”. First position: NRC post-doctoral fellow, NASA-Ames Research Center, Moffett Field, California. Second position: Professor of Radiology, UC San Francisco. Current Position: Retired
  3. Daniel R. Upper (1996, Mathematics, University of California, Berkeley) “Theory and Algorithms for Hidden Markov Models and Generalized Hidden Markov Models”. First position: NSF Graduate Fellow.
  4. Rajarshi Das (1997, Computer Science, Colorado State University) “The Evolution of Emergent Computation in Cellular Automata”. First position: NSF Postdoctoral Fellow. Current position: Research Scientist, IBM TJ Watson Research Center, Hawthorne, NY.
  5. David Feldman (1998) “Computational Mechanics of Classical Spin Systems”. First position: Assistant Professor of Physics, College of the Atlantic, Bar Harbor, Maine. Current position: Professor of Physics, Mathematics, and Computer Science, College of the Atlantic.
  6. Wim Hordijk (1999, Computer Science, University of Mexico) “Mechanisms of Emergent Computation in Cellular Automata”. First position: Postdoctoral researcher. Current position: Research Scientist, Konrad Lorenz Insitute, Vienne, Austria.
  7. Erik van Nimwegen (1999, Physisc cum laude, University of Utrecht) “Statistical Dynamics of Epochal Evolution”. First position: Postdoctoral researcher, Center for Physics and Biology, Rockefeller University, New York. Current position: Professor of Biophysics, University of Basel.
  8. Cosma Shalizi (2001, University of Wisconsin, Madison) “Causal Architecture, Complexity and Self-Organization in the Time Series and Cellular Automata”. First position: Postdoctoral researcher, Santa Fe Institute. Current position: Professor, Statistics Department, Carnegie Mellon University.
  9. Dowman P. Varn (2001, University of Tennessee, Knoxville) “Language Extraction from ZnS”. First position: Postdoctoral Fellow, Max Planck Institute for Complex Systems, Dresden, Germany. Current position: Lecturer, Mathematics, UC Davis.
  10. David Albers (2004, University of Wisconsin, Madison) “A Qualitative Numerical Study of High Dimensional Dynamical Systems”. First position: Postdoctoral Researcher, Columbia University Medical School, New York. Current position: Research Scientist, Columbia University Medical Center.
  11. Christopher Streliof (2007, University of Illinois, Urbana-Champaign) “Inferring Markov Chains: Bayesian Estimation, Model Comparison, Entropy Rate, and Out-of-class Modeling”. First position: Postdoctoral Researcher, Michigan State University, Lansing. Current position: Postdoctoral Researcher, University of California, Los Angeles.
  12. Sean Whalen (2010, Computer Science) “Security Applications of the e-Machine”. First position: Postdoctoral researcher, Computer Science at Columbia University. Second position: Postdoctoral researcher, Computational Biology, Mt. Sinai Hospital, New York City. Current position: Research scientist, UC San Francisco.
  13. John Mahoney (2010) “Extensions of the Theory of Computational Mechanics”. First position: Postdoctoral Researcher, University of California, Merced. Current position: Research Physicist, University of California, Davis.
  14. Christopher Ellison (2011) “Structural Complexity in Stationary Stochastic Dynamical Systems”. First position: Postdoctoral Fellow, Wisconsin Institute for Discovery. Current position: Staff Scientist, IsoInvest, Chicago, IL.
  15. Ryan James (2013) “Anatomy of a Bit: Information in a Time Series Measurement”. First position: Postdoctoral Researcher, University of Colorado, Boulder. Second position: Postdoctoral Researcher, University of California, Davis; current position: Senior Research Engineer, reddit.com.
  16. Nicholas Travers (2013, Mathematics) “Bounds on Convergence of Entropy Rate Approximations in Hidden Markov Processes”. First position: Postdoctoral Researcher, Technion University, Israel. Current position: Professor, Mathematics, University of Indiana, Bloomington.
  17. Richard Watson (2014, Mathematics) “The Structure of Transient Memory in a Simple Model of Inhibitory Neural Feedback”
  18. Nix Barnett (2016, Mathematics) “Mechanisms within the Black Box: Prediction, Computation, Randomness, and Complexity of Input-Output Processes via the ε-Transducer”.
  19. Sarah E. Marzen (2016, Ph.D. in Physics, University of California, Berkeley) “Bio-inspired problems in rate-distortion theory”; NSF Graduate Fellow, UCB Chancellors Fellow.
  20. Paul Michael Riechers (2016) “Exact Results Regarding the Physics of Complex Systems via Linear Algebra, Hidden Markov Models, and Information Theory.” First and current position: Postdoctoral researcher, UC Davis.
  21. Alexander Boyd (2017) “Thermodynamics of Correlations and Structure in Information Engines”. First and current position: Post-doctoral researcher, Centre for Quantum Technologies, Singapore.
  22. Cina Aghamohammadi (2018) “Memory and Thermodynamic Costs of Sampling and Biased Sampling”.
  23. Jordan Snyder (2018, Mathematics) “Collective Behavior in Dynamics on Networks”. Current position: Postdoctoral researcher, Applied Mathematics, Unversity of Washington.
  24. Xincheng Lei (2019) “Information Transport in One-dimensional Localized Systems”. First position: Applied Scientist at Amazon, Palo Alto, CA.
  25. Adam Rupe (2020) “A Behavior-Driven Theory of Emergent Pattern and Structure in Complex Spatiotemporal Systems”.  First position: Postdoctoral researcher, Center for Nonlinear Studies, Los Alamos National Laboratory; current position: Postdoctoral researcher, Pacific Northwest National Laboratory.
  26. Jeffrey Emenheiser (2020) “Identification and analysis of patterns in collective systems”.
  27. Alexandra Jurgens (2021) “On Infinite Complexity: Quantifying the Randomness and Structure of Hidden Markov Processes”. First position: Postdoctoral researcher at University of California, Davis; Current position: Postdoctoral researcher at INRI Bordeaux, France.
  28. Samuel Loomis (2021) “Invariant Properties of Ergodic Processes, with applications to Quantum Computing, Data Science, and Emissions Modeling” Current position: Staff scientist, Syngenta
  29. Mikhael Semaan (2022) “Nonequilibrium Fluctuations and Information Processing in Mesoscopic Complex Systems”. Current position: Lecturer, Physics, University of Utah.
  30. Kyle Ray (2023) “Computing with Physical Systems”. First position: Postdoctoral researcher, Physics, UC Davis.
  31. Ariadna Venegas-Li (2023) “Measured Quantum-State Stochastic Processes”.
  32. Gregory Wimsatt (2023) “Harnessing Fluctuations in Thermodynamic Computing via Time-Reversal Symmetries”
  33. David Gier (2023) “Stochastic Quantum Information Processing with Separable Qudit Processes”; NSF Graduate Fellow.