Property:Biography
From Thermodynamics of Computation
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Andrew Adamatzky is Professor of Unconventional Computing and Director of the Unconventional Computing Laboratory, Department of Computer Science, University of the West of England, Bristol, UK. He does research in molecular computing, reaction-diffusion computing, collision-based computing, cellular automata, slime mould computing, massive parallel computation, applied mathematics, complexity, nature-inspired optimisation, collective intelligence and robotics, bionics, computational psychology, non-linear science, novel hardware, and future and emergent computation. He authored seven books, mostly notable are `Reaction-Diffusion Computing’, `Dynamics of Crow Minds’, `Physarum Machines’, and edited twenty-two books in computing, most notable are `Collision Based Computing’, `Game of Life Cellular Automata’, `Memristor Networks’; he also produced a series of influential artworks published in the atlas `Silence of Slime Mould’. He is founding editor-in-chief of ‘J of Cellular Automata’ and “ J of Unconventional Computing’ and editor-in-chief of “J Parallel, Emergent, Distributed Systems’ and ‘Parallel Processing Letters’. +
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Christian van den Broeck is physicist working in the fields of stochastic thermodynamics and soft matter. +
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David H. Wolpert is an American mathematician, physicist and computer scientist. He is a professor at Santa Fe Institute. He is the author of three books, three patents, over one hundred refereed papers, and has received numerous awards. His name is particularly associated with a group of theorems in computer science known as "no free lunch". +
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Erik Winfree (born September 26, 1969) is an American computer scientist, bioengineer, and professor at California Institute of Technology. He is a leading researcher into DNA computing and DNA nanotechnology. (Wikipedia) +
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External Professor, Santa Fe Institute
Co-Founder of Open Hazards Group and Chair of the Board
Distinguished Professor of Physics and Geology, University of California, Davis
John is the Executive Director Emeritus of the APEC Cooperation for Earthquake Simulations (ACES)
John is a Senior Advisor to the Association of Pacific Rim Universities (APRU)
John is a Visiting Professor at Tohoku University in Sendai, Japan
John was a Visiting Associate at the California Institute of Technology (1980-1982) at the Caltech Seismological Laboratory. He was also a Member (1990 - 1997) and Chair (1994-1996) of the scientific Advisory Council to the Southern California Earthquake Center. He is currently a Distinguished Visiting Scientist at the Jet Propulsion Laboratory, Pasadena, CA (1995-present), an External Professor at the Santa Fe Institute, and a Fellow of the American Physical Society (2005), the American Geophysical Union (2008), and the American Association for the Advancement of Science (2017). Recently, he was a co-winner of the NASA Software of the Year Award (2012). John received his B.S.E from Princeton University (Magna Cum Laude, Phi Beta Kappa, Tau Beta Pi), and M.S. (1973) and Ph.D. (1976) from the University of California at Los Angeles. In addition to natural hazards and earthquakes, he also has professional interests in forecasting, validation of forecasts, and quantitative finance. He currently co-organizes (along with Michael Maouboussin, Chris Wood and Martin Lebowitz) a yearly meeting on risk for the Santa Fe Institute, often held at Morgan Stanley, Inc., in New York. He teaches courses in Risk and Natural Disasters; Complex Systems; and Econophysics and Quantitative Finance at the University of California, Davis. +
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Gavin E. Crooks is an English chemist currently researching in America. He is known for his work on non-equilibrium thermodynamics and statistical mechanics. He discovered the Crooks fluctuation theorem, a general statement about the free energy difference between the initial and final states of a non-equilibrium transformation. Crooks is now a senior research scientist at Rigetti Computing. +
Gašper Tkačik is a theoretical physicist who studies information processing in living systems. He uses tools from statistical physics of disordered systems and from information theory to investigate biological systems such as networks of neurons or genes. The unifying hypothesis driving his research has been that information processing networks have evolved or adapted to maximize the information transmitted from their inputs to the outputs, given the biophysical noise and resource constraints. He works closely with experimentalists and analyzes data sets that record simultaneously the behavior of many network components. Results of his work gave insight into the principles of genetic regulation in early morphogenesis of Drosophila and of information coding in retinal ganglion cells. In the future, he plans to expand his activities to study collective behavior and cellular self-organization. +
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I am a scientist working at the intersection of physics, biology, and the earth sciences. Using mathematical and computational techniques I study how simple theoretical principles inform a variety of phenomena ranging from major evolutionary life-history transitions, to the biogeography of plant traits, to the organization of bacterial communities. I am particularly interested in biological architecture as a mediator between physiology and the local environment. +
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I am an Assistant Professor in the Departments of Computer Science and Mathematics at the University of Colorado at Boulder, where I am a member of the CS Theory Group.
My research has two main thrusts (with deep underlying relations beneath): Interactions between theoretical computer science and mathematics (particularly algebraic geometry, representation theory, and group theory), and Developing the theory of complex systems and complex networks, and applying this theory with my collaborators in a variety of fields, such as ecology, evolutionary biology, economics, climate, and beyond. I'm always looking for new problems that need new theory! I was previously an Omidyar Fellow at the interdisciplinary Santa Fe Institute for complex systems. Prior to SFI, I was a postdoc in the University of Toronto CS Theory Group, and prior to that I got my Ph.D. at the University of Chicago. +
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I am interested in computational complexity theory and design of algorithms, and their applications in bioinformatics, biomolecular computation, hardware verification, and combinatorial auctions. Much of my current work focuses on prediction of the secondary structure of nucleic acids from the base sequence, informed by thermodynamic energy models, as well as applications of prediction tools to design of biomolecules. +
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I am interested in how natural systems manipulate and process information, producing new forms of self-organization.
As a biophysicist I have pursued these questions primarily in neuroscience. I think about the brain as a statistical computational device and seek to uncover the principles that underlie the organization of neural circuits across scales from cells to the whole brain. I have worked on systems in the brain that support many different functions: vision, audition, olfaction, spatial cognition, motor control and decision making. Applying lessons about adaptive molecular sensing from the olfactory system, I have also written about the functional organization of the adaptive immune system in vertebrates and bacteria (CRISPR).
As a theoretical physicist, I have pursued questions about the fundamental nature of space and time. I have worked on the apparent loss of quantum information in the presence of black holes and the origin of entropy and thermodynamics in gravitating systems. I have discussed how the familiar smooth structure of space-time can emerge as a long-distance effective description of more complex underlying physical constructs. I have shown how some dimensions of space can be regarded as emergent, arising from the quantum entanglement and information structure of an underlying lower-dimensional theory.
Finally, I have written on problems in statistical inference and machine learning, and in particular on “Occam’s Razor”, i.e., the tradeoff between simplicity and accuracy in quantitative models. I am interested in this question because all scientific theories involve fitting models to data, and there is a fundamental tradeoff between the complexity of models and their ability to generalize correctly to new situations. This tradeoff influences how scientists infer models of the world, how machines learn the structure in data, and how living things from the scale of single cells to entire organisms with brains adapt to their environment over timescales from milliseconds to evolutionary time.
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I am just starting a Royal Society University Research Fellowship in the Bioengineering Department, where I will be building up a "Principles of Biomolecular Systems" group. I've previously been affiliated with Nick Jones' systems and signals group, the Doye / Louis biophysics groups in Oxford and the biochemical networks group of Pieter Rein ten Wolde in Amsterdam. My group probes the fundamental principles underlying complex biochemical systems through theoretical modelling, simulation and experiment. In particular, We focus on the interplay between the detailed biochemistry and the overall output of a process such as sensing, replication or self-assembly. We're inspired by natural systems, and aim to explore the possibilities of engineering artificial analogs. For more details, please refer to my Research Page. +
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I earned my PhD in Physics with John C. Baez at UC Riverside, developing a black-box semantics for open chemical reaction networks using techniques from category theory. I'm currently working as a postdoctoral researcher at NIST working with the Smart Grid team on mitigating complexity in multi-scale modeling. +
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I strive to extract the non-equilibrium physical principles behind chemical and biological processes. In addition, I am very interested in applying those principles to design new functional materials, smart active self-assemblies, and molecular machines. +
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I work at the interfaces between computer science, physics, and biology which provide some of the most challenging problems in today’s science and technology. We focus on organizing computational principles that govern information processing in biology, at all levels. To this end, we employ and develop methods that stem from statistical physics, information theory and computational learning theory, to analyze biological data and develop biologically inspired algorithms that can account for the observed performance of biological systems. We hope to find simple yet powerful computational mechanisms that may characterize evolved and adaptive systems, from the molecular level to the whole computational brain and interacting populations. +
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I'm currently a PhD student at the School of Engineering at Brown University. My interests lie at the intersection of geometric phenomena in quantum systems, conformal field theory, quantum thermodynamics, and condensed matter theory. In particular, I'm deeply interested in geometric properties of Gorini-Kossakowski-Sudarshan-Lindblad (GKSL) dynamics and their applications to condensed matter systems, quantum information processing, and classical information processing. I'm also interested in the properties of conformal field theories (CFTs) out of equilibrium, and the ways by which nonequilibrium CFT phenomena manifest in condensed matter models. Finally, I'm interested in the consequences that renormalisation group transformations have for resource theories.
'''At present''', my work focuses on applications of Gorini-Kossakowski-Lindblad-Sudarshan (GKSL) dynamics with multiple steady states, resource theories, and shortcuts-to-adiabaticity to physical models for reversible computing and conformally invariant systems. ''Reversible computing'' is a paradigm of computing that relies on preserving and unwinding correlations, which allows us to avoid the energy cost resulting from irretrievably ejecting information stored in memory devices into the environment. Although systems implementing reversible logic were first proposed as early as 1978 by Fredkin and Toffoli; designing a model of fast, fully adiabatic, and scalable classical reversible operations remains an ongoing and active area of interest. '''''Here''''', the language of GKSL dynamics, shortcuts-to-adiabaticity, resource theories, and quantum speed limits are especially suited to helping us design our desired models for reversible computing. I'm currently working alongside several others to develop these models.
'''My other work''' focuses on the consequences that conformal invariance can have for resource theories, as well as the lessons resource theories can have for conformally invariant systems. Recent results by Bernamonti ''et al.'' for holographic second laws, Guarnieri ''et al.'' for relationships between stochastic quantum work techniques and resource theories, and Faist and Renner on new information measures for the work cost of quantum processes, and Albert ''et al.'' on the geometric properties of Lindbladians themselves have substantial implications for systems described by CFTs. '''''My interest here''''' is in examining what lessons these results have for CFTs: in particular, understanding whether stochastic quantum work techniques can be expressed for CFTs via the holographic second laws, where an extension to the holographic second laws can be developed using this new information measure, and what lessons we may derive for CFTs out of equilibrium with degenerate steady states.
'''Before my current appointment''', I was a research fellow and visiting faculty member at the Department of Applied Mathematics at Flame University. I received my M.Sci. in physics from Carnegie Mellon University in 2016, and my B.Sci. in physics from Carnegie Mellon University in 2014. There, I worked under Di Xiao on optoelectronic phenomena on the surfaces of topological insulators, in particular examining properties of the photogalvanic effect on the surfaces of topological insulators at zero and finite temperature. I also had the brief opportunity to work on curve fitting for experimental soft condensed matter physics under Stephanie Tristram-Nagle, as well as on analytic analysis of the dynamical RG flow of the Ising model under Robert Swendsen.
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Ilya Mark Nemenman (born January 8, 1975 in Minsk, Belarus) is a theoretical physicist at Emory University, where he is a Winship Distinguished Research Professor of Physics and Biology. He is known for his studies of information processing in biological systems and for developing course-grained models of these systems. He is a Fellow of the American Physical Society for "his contributions to theoretical biological physics, especially information processing in a variety of living systems, and for the development of coarse-grained modeling methods of such systems" . He also has served in the chair line of the division of biological physics, from 2013-2018. Nemenman also was a founder of the q-bio conference, and is a general member of the Aspen Center for Physics. (Wikipedia) +
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James P. Crutchfield (born 1955) is an American mathematician and physicist. 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. He is currently a Professor of Physics at the University of California, Davis, where he 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. From 1985 to 1997, he was a Research Physicist in the Physics Department at the University of California, Berkeley. 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. +
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Manoj Gopalkrishnan is a faculty member in Electrical Engineering at the Indian Institute of Technology Bombay. He received his Ph.D. in Computer Science from the University of Southern California in 2008 working with Professor Leonard Adleman. His Ph.D. thesis was titled "Theoretical and Experimental Self-Assembly." +
Massimiliano Esposito is a physicist working in the field of stochastic thermodynamics. See his website for more information. +
