Property:Biography
From Thermodynamics of Computation
This is a property of type Text.
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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. +
My first exposure to the thermodynamics of computation was in 1985 when I read a Scientific American article (1) about the subject during high school. Then in 1988, I took the world's first class on Nanotechnology, taught by the visionary K. Eric Drexler at Stanford University, in which Drexler discussed an architecture (2-4) for reversible nanomechanical computation which he was designing at MIT. I graduated from Stanford in 1991 with distinction with a B.Sci. in Symbolic Systems (focusing on CS and AI), and went on to do my graduate work in the EECS department at MIT. After completing a Masters thesis on decision-theoretic methods in AI (5), I decided that we really needed faster computers and so, with support from an NSF Graduate Fellowship, I began studying nanoscale computation in earnest. In 1995 I designed the world's first method for universal computation using DNA chemistry (6); this method was required to be reversible for fundamental thermochemical reasons. I then joined the DARPA-funded MIT Reversible Computing Project, led by the legendary hacker (7) Tom Knight, and cellular automata expert (8) Norm Margolus, who was an alumnus of the Information Mechanics group (9) led by Ed Fredkin, former director of the MIT Lab for Computer Science and inventor of the Billiard-Ball Model of computation (10), which was the first ballistic model of reversible computation. Knight and his student Saed Younis had invented the first complete fully adiabatic and reversible CMOS-based logic technology (11), and in the subsequent project, I and other students built on their work to create the first adiabatic and fully-reversible universal processor chips (12,13). During this period I also showed that the aggregate performance of parallel 3D adiabatic reversible machines scales better physically (by polynomial factors), within power dissipation constraints, than any possible irreversible computing architecture (14-15). My 1999 MIT doctoral dissertation (16) derived several other related scaling results, and explored a number of other aspects of reversible computing, from physics through application algorithms. After graduating, I continued my research in faculty positions at the University of Florida (17) and Florida State University (18), and currently as a senior-level engineering scientist at Sandia National Laboratories (19). While at UF and FSU, I occasionally taught a research survey course on The Physical Limits of Computation (20). In 2005 I organized the first workshop on reversible computing (21) and I currently serve on the program committee for the ongoing Reversible Computation conference series (22). Recently, I have reviewed and clarified the foundations of Landauer's principle (23-25) and reversible computing theory (26-28), and I am also presently working on a couple of funded R&D projects at Sandia which are aiming to demonstrate new engineering implementations of reversible computing (29). Numerous of my other papers and talks in this field are available through my research webpages (17-19).
My general interest is the statistical physics of disordered systems. A system can be disordered either because each particle (or spin, or neuron, or economic agent...) is different from all other ones, or because it sees a different environment: generally this happens in a glassy phase, in which the various particles freeze in some positions which look random, and don't have the periodicity of a crystal. In these cases it turns out to be very difficult to even understand the basics of the collective behaviour, such as the phase diagram. This field has been one of the main developments of statistical physics in the last two or three decades. In classical physics, it basically started with spin glasses, and some offsprings developed gradually towards such diverse systems as combinatorial optimization problems, error correcting codes, neural networks, structural glasses, or systems of interacting economic agents with heteronegeous strategies. +
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My research applies combinatorial and graph theoretic tools to various computational domains. My recent work has focused on the following domains: designing algorithms for shortest paths, pattern detection and other computational problems in graphs and matrices, reducing fundamental computational problems to one another in a fine-grained way, sometimes showing equivalences, studying how much graph distance information can be compressed, and computational issues in social choice: when and how can one efficiently manipulate elections, tournaments and competitions, how to measure the quality of a voting rule, etc. +
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My research group and I focus on statistical mechanics and thermodynamics at the molecular level, with a particular emphasis on far-from-equilibrium phenomena. We have worked on topics that include the application of statistical mechanics to problems of biophysical interest; the analysis of artificial molecular machines; the development of efficient numerical schemes for estimating thermodynamic properties of complex systems; the relationship between thermodynamics and information processing. We also have interests in dynamical systems, quantum thermodynamics, and quantum and classical shortcuts to adiabaticity. +
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Nonequilibrium statistical physics (theory) and related fields. Keywords: Thermodynamics of information, fluctuation theorems, quantum thermodynamics. Quantum information theory (quantum estimation etc), quantum open systems, quantum feedback control. Theoretical biophysics (molecular motors etc). +
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Originally, my main research focus has been the statistical physics of disordered systems and the study of the collective behavior of complex assemblies of simple elementary components. This includes glassy systems such as spin glasses, random polymers or a jammed ensemble of colloids, quantum problems but also simpler models in a out-of-equilibrium situation, for instance ferromagnets quenched at their critical points. It turns out that this problematic naturally arises in other fields than physics, and in particular in computer science problems as diverse as combinatorial optimization, statistical inference, machine learning and signal processing. This has become over the last few years a rapidly evolving and exciting field at the interface of many disciplines, which we study together with the SPHINX group. +
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Our group is interested in discovering design principles that govern the structure and function of neurons and neural circuits. We record from well-defined neurons, mainly in flies’ visual systems, to measure the molecular and cellular factors that determine relevant measures of performance, such as representational capacity, dynamic range and accuracy. We combine this empirical approach with modelling to see how the basic elements of neural systems (ion channels, second messengers systems, membranes, synapses, neurons, circuits and codes) combine to determine performance. We are investigating four general problems. How are circuits designed to integrate information efficiently? How do sensory adaptation and synaptic plasticity contribute to efficiency? How do the sizes of neurons and networks relate to energy consumption and representational capacity? To what extent have energy costs shaped neurons, sense organs and brain regions during evolution? +
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Peter Stadler is a professor of bioinformatics at Leipzig. +
Pier Luigi Gentili is Ph.D. in Chemistry. His research and teaching activities are focused on Complex Systems. He is trusting in Natural Computing as an effective strategy to understand Complex Systems and face the Computational Complexity Challenges. In particular, he is developing the innovative Chemical Artificial Intelligence. He has several collaborations and work experience in many laboratories. For instance, the "Photochemistry and Photophysics Group" of the University of Perugia (Italy); the "Nonlinear Dynamics Group" of the Brandeis University (USA); the "European Laboratory of Nonlinear Spectroscopy" in Florence (Italy); the "Center for Photochemical Sciences" of the Bowling Green State University (USA); the "Laboratory of Computational Chemistry and Photochemistry" of University of Siena (Italy).
ORCID: 0000-0003-1092-9190. +
Pieter Rein ten Wolde is a group leader at the FOM Institute AMOLF and a professor in the Department of Physics at the Vrije Universiteit of Amsterdam, Netherlands. He performed his Ph.D. research on the dynamics of first-order phase transitions at the FOM Institute AMOLF under the guidance of Prof. Daan Frenkel. After he obtained his Ph.D. in 1998, he did a postdoc in the group of David Chandler at the University of California, Berkeley, working on hydrophobic interactions. In 2001, he returned to AMOLF, where he set up a research group that combines theory and computer simulations to elucidate the design principles of biochemical networks. His current research interests include information transmission in cellular systems and the thermodynamics of cellular computation. +
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Professor Qian (Q=Ch) received his B.A. in Astrophysics from Peking University in China in 1982, and his Ph.D. in Biochemistry and Biophysics from Washington University School of Medicine in St. Louis in 1989. Subsequently, he worked as postdoctoral researcher at University of Oregon and Caltech on biophysical chemistry and mathematical biology. Before joining the University of Washington, he was an assistant professor of Biomathematics at UCLA School of Medicine. From 1992-1994, he was a fellow with the Program in Mathematics and Molecular Biology (PMMB), a NSF-funded multi-university consortium.
Professor Qian's main research interest is the mathematical approach to and physical understanding of biological systems, especially in terms of stochastic mathematics and nonequilibrium statistical physics. In recent years, he has been particularly interested in a nonlinear, stochastic, open system approach to cellular dynamics. Similar population dynamic approach can be applied to other complex systems and processes, such as those in ecology, infection epidemics, and economics. He believes his recent work on the statistical thermodynamic laws of general Markov processes can have applications in ecomomic dynamics and theory of values. In his research on cellular biology, his recent interest is in isogenetic variations and possible pre-genetic biochemical origins of oncogenesis. +
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Riccardo Zecchina is theoretical physicist at Bocconi University among other appointments. His areas of study include "Statistical physics, machine learning, computational neuroscience and computational biology, inverse dynamical problems, distributed algorithms for optimisation, constraint satisfaction problems and statistical inference, information theory, graphical games, …" +
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Sadasivan Shankar is Research Technology Manager of Microelectronics in SLAC National Laboratory, Adjunct Professor in Materials Science and Engineering in Stanford University . He was the first Margaret and Will Hearst Visiting Lecturer in Harvard University. He has co-instructed several graduate-level classes on Computational Materials Design, Extreme Computing for Real Applications, and Mitigating Toxicity by Materials Design. He is involved in research in the areas of materials, chemistry, multi-scale and non-equilibrium methods, and large-scale computational methods. Dr. Shankar earned his Ph.D. in Chemical Engineering and Materials Science from University of Minnesota, Minneapolis. He is a co-inventor in over twenty patent filings covering areas in new chemical reactor designs, semiconductor processes, bulk and nano materials, device structures, and algorithms. He is also a co-author in over hundred publications and presentations in measurements, multi-scale and multi-physics methods spanning from quantum scale to macroscopic scales, in the areas of chemical synthesis, plasma chemistry and processing, non-equilibrium electronic, ionic, and atomic transport, energy efficiency of information processing, and machine learning methods for bridging across scales, and estimating complex materials properties and in process control. Dr. Shankar is a co-founder of Material Alchemy, a “last mile” translational and independent venture in materials design for accelerating materials discovery to adoption, with environmental sustainability as a key goal. +
Shamit is a Senior Research Associate at the Rosalind Franklin Institute under the research theme "imaging with light and sound". He is also affiliated with the department of engineering science at the University of Oxford. Shamit is developing photonic and acoustic tools for monitoring and controlling the state of biological materials. He is also synthesising functional nano-systems that can be controlled remotely via these tools. These tools and systems will allow to apply the laws of thermodynamics as design principles to solve major challenges in biomedical, environmental and computational sciences.
The vision is motivated by the extraordinary properties of sound waves in lipid interfaces that were established for the first time during Shamit's Ph.D. thesis at Boston University. The research strongly supports the possibility of sound being a physical basis for the phenomenon of nerve pulse propagation, and has been highlighted in the “Revolutions in Science” edition of the Scientific American and on the cover of German science magazine Spektrum. The research is fundamentally related to the thermodynamics and energy efficiency of computing in a single neuron. +
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Statistical physics and its applications in computer science and technology. Phase transitions and algorithmic hardness in optimization, statistical inference, machine learning and signal processing problems. Message passing algorithms. Physics of systems with glassy behavior. Physics of algorithms on random structures. +
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Terrence Sejnowski is a pioneer in computational neuroscience and his goal is to understand the principles that link brain to behavior. His laboratory uses both experimental and modeling techniques to study the biophysical properties of synapses and neurons and the population dynamics of large networks of neurons. New computational models and new analytical tools have been developed to understand how the brain represents the world and how new representations are formed through learning algorithms for changing the synaptic strengths of connections between neurons. He has published over 300 scientific papers and 12 books, including The Computational Brain, with Patricia Churchland.
He received his PhD in physics from Princeton University and was a postdoctoral fellow at Harvard Medical School. He was on the faculty at the Johns Hopkins University and he now holds the Francis Crick Chair at The Salk Institute for Biological Studies and is also a Professor of Biology at the University of California, San Diego, where he is co-director of the Institute for Neural Computation and co-director of the NSF Temporal Dynamics of Learning Center. He is the President of the Neural Information Processing Systems (NIPS) Foundation, which organizes an annual conference attended by over 1000 researchers in machine learning and neural computation and is the founding editor-in-chief of Neural Computation published by the MIT Press.
An investigator with the Howard Hughes Medical Institute, he is also a Fellow of the American Association for the Advancement of Science and a Fellow of the Institute of Electrical and Electronics Engineers. He has received many honors, including the NSF Young Investigators Award, the Wright Prize for interdisciplinary research from the Harvey Mudd College, the Neural Network Pioneer Award from the Institute of Electrical and Electronics Engineers and the Hebb Prize from the International Neural Network Society. He was elected to the Institute of Medicine in 2008, to the National Academy of Sciences in 2010, and to the National Academy of Engineering in 2011. He is one of only 10 living persons to be a member of all 3 national academies.
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The Jolivet group at the University of Geneva, Switzerland and CERN, is interested in the energetic cost of information processing in the brain, and more generally, in brain energy metabolism. +
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The Sivak group uses theory and computation to study the nonequilibrium thermodynamics of molecular-scale biological processes. We seek to identify the physical constraints placed on biological system +
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Udo Seifert is physicist working on stochastic thermodynamics and information processing in biology. +
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William Bialek (born 1960 in Los Angeles, California) is a theoretical biophysicist and a professor at Princeton University and The Graduate Center, CUNY. Much of his work, which has ranged over a wide variety of theoretical problems at the interface of physics and biology, centers around whether various functions of living beings are optimal, and (if so) whether a precise quantification of their performance approaches limits set by basic physical principles. Best known among these is an influential series of studies applying the principles of information theory to the analysis of the neural encoding of information in the nervous system, showing that aspects of brain function can be described as essentially optimal strategies for adapting to the complex dynamics of the world, making the most of the available signals in the face of fundamental physical constraints and limitations. (Wikipedia) +
Wojciech Hubert Zurek (born 1951) was educated in Kraków, Poland (M.Sc. 1974) and Austin, Texas (Ph.D. 1979). He spent two years at Caltech as a Tolman Fellow, and started at Los Alamos as an Oppenheimer Fellow. He was the leader of the Theoretical Astrophysics Group at Los Alamos from 1991 until he was elected a Laboratory Fellow in the Theory Division in 1996. Zurek served as a member of the external faculty of the Santa Fe Institute, where he founded the Complexity, Entropy, and Physics of Information network, and has been a visiting professor at the University of California, Santa Barbara, where he co-organized the Quantum Coherence and Decoherence and the Quantum Computing and Chaos Programs at UCSB's Institute for Theoretical Physics. In 2005 he won the Alexander von Humboldt Prize, and in the 2004/2005 academic year he was a Phi Beta Kappa Visiting Lecturer. In 2009 Wojciech Zurek was awarded the Marian Smoluchowski Medal of the Polish Physical Society in recognition of his work on the quantum-classical transition. Developing theory of decoherence and elucidating its significance for the quantum–to–classical transition is Zurek’s major contribution to physics. Zurek also demonstrated (with Wootters) that an unknown quantum cannot be cloned. This is a fundamental result, and an essential distinction between classical and quantum information. Furthermore, Zurek has vastly extended Kibble’s cosmological scenario to develop a successful general theory of phase transition dynamics (“Kibble-Zurek mechanism”) that has been verified experimentally in superconductors and superfluids (including BEC’s). +
