EPS SNPD Prizes 2019
The Statistical and Nonlinear Physics Division (SNPD) of the European Physical Society is happy to announce the winners of the two prizes of the Division:
The EPS Statistical and Nonlinear Physics Prize 2019 is awarded to:
- Sergio Ciliberto (ENS Lyon) and
- Satya Majumdar (Paris-Sud)
The EPS-SNPD Early Career Prize 2019 is awarded to:
- Karel Proesmans (Hasselt) and
- Valentina Ros (ENS Paris)
The prize award ceremony will be during the 2nd conference of the EPS Statistical and Nonlinear Physics Division at Nordita/Stockholm 7-11 May 2019. Visit the conference website here for more information.
Long citations and biographies
Sergio Ciliberto “for his seminal contributions over a wide range of problems in statistical and nonlinear physics, in particular for performing groundbreaking new experiments testing Flucn tuation Theorems for injected power, dissipated heat, and entropy production rates, as well as investigating experimentally the connection between dissipated heat and the Landauer bound, thus demonstrating a link between information theory and thermodynamics.“
Sergio Ciliberto studied physics in Florence and was a researcher at the Istituto Nazionale Ottica in Florence 1982-1990. He also spent some time as a postdoctoral researcher and visiting scientist in Orsay, at the University of Pennsylvania (Haverford College), and at the Center of Nonlinear Studies at Los Alamos National Laboratory. In 1991 he was appointed at the Laboratoire de Physique at ENS Lyon, being the Director of the Lab from 2000-2006. Part of his research was supported by a large ERC grant with the title “Out of equilibrium fluctuations in confined phase transitions”. From 2012-2014 he was Vice President of ENS Lyon in charge of research.
Sergio Ciliberto is an outstanding experimentalist whose work had a profound impact on several areas of statistical and nonlinear physics. Over his scientific career, he has explored many very different physical systems, and was able to make very significant contributions over a wide range of problems. He has tested many innovative theoretical ideas in real physical situations, which led him to demonstrate the relevance of several deep concepts. He initially worked in quantum optics, then later he investigated order-chaos transitions in nonlinear systems, and worked in fluid turbulence, as well as on crack dynamics in heterogeneous materials and aging of amorphous materials. He then continued with his ground-breaking investigations on fluctuations of the injected and dissipated power in out-of-equilibrium systems. This was followed by his investigations of the connections between information theory and thermodynamics (experimental tests of the Landauer principle). Sergio Ciliberto also performed one of the first measurements of Liapunov exponents from an experimental time series, at a time when this topic was entirely new in the scientific community.
Satya Majumdar “for his seminal contributions to non-equilibrium statistical physics, stochastic processes, and random matrix theory, in particular for his groundbreaking research on Abelian sandpiles, persistence statistics, force fluctuations in bead packs, large deviations of eigenvalues of random matrices, and applying the results to cold atoms and other physical systems.“
Satya Majumdar got his PhD in Physics in 1992 from the Tata Institute of Fundamental Research in Bombay, India. He then spent some time in the USA, first as a postdoctoral fellow at AT&T Bell Labs and then at Yale University. After returning to the Tata Institute as a faculty (1996-1999), in 2000 he was appointed as a CNRS scientist at the Universite´ Paul-Sabatier (Toulouse, France), to be followed in 2003 by his appointment as Directeur de Recherche in CNRS at the University Paris-Sud, Orsay. Besides this, he also holds several honorary positions as an adjunct professor, at the Tata Institure in Bombay, at the Weizmann Institute in Rehovot, Israel, at the Higgs Centre of the University of Edinburgh, and at the Raman Research Institute in Bangalore, India.
Satya Majumdar made outstanding contributions in many different subject areas of statistical physics, stochastic processes, and random matrix theory, with applications ranging from granular systems all the way to computer science and biology. His work is characterised by a very elegant mathematical analysis of physical problems, leading to a plethora of beautiful analytical results. These have provided deep insights into the underlying physics, but often also advance fundamental mathematical understanding. His work is probably almost as well known to probabilists and mathematical physicists as it is to statistical physicists. Some of his most important contributions are on self-organized criticality of sandpile models, on stress propagation in granular systems, on persistence and first-passage properties in nonequilibs rium systems, on sorting and search problems in computer science, and on extreme value statistics of correlated random variables. His seminal work in random matrix theory has applications for growth models, biological sequence matching, conductance distributions in quantum dots, and much more.
Karel Proesmans “for his outstanding research contributions in the field of stochastic thermodynamics, in particular his work dealing with optimization protocols for thermal engines, as well as his work on thermodynamic uncertainty relations for discrete-time and periodically driven systems.“
Karel Proesmans obtained his PhD in 2017 at the University of Hasselt for a thesis entitled ’Efficiency of stochastic engines’, under the supervision of Bart Cleuren and Christian Van den Broeck. During his PhD work, he also collaborated with John Bechhoefer (Simon Fraser University). After his PhD he did a research visit of six months to the College` de France in Paris under the supervision of Bernard Derrida and another one of three months to the University of Cambridge under the supervision of Daan Frenkel. Currently he is a Postdoctoral Researcher at Hasselt University.
Most of his work focuses on stochastic thermodynamics. One project is about stochastic efficiency, in which he, together with his collaborators, studies the general properties of the efficiency fluctuations in a thermodynamic system. In another work he has derived a framework to study the linear thermodynamics of time-dependent, stochastic systems. He showed that these systems should satisfy an Onsager-Casimir symmetry and looked at the implications on the performance of the engine (power output, efficiency, amount of dissipation). While these projects were mainly theoretical, the results have also been verified experimentally. He is also interested in the thermodynamic uncertainty relation and the properties of persistent random walks in higher dimensions.
Valentina Ros “for her outstanding research contributions in quantum and classical disordered systems, explaining new ways in which those systems can break ergodicity and fail to equilibrate, and her investigations of rough, high-dimensional landscapes emerging in this context. “
Valentina Ros is a Postdoctoral Researcher at the Laboratoire de Physique of the Ecole´ Normale Superieure´ in Paris since
November 2018. She obtained her PhD in Statistical Physics in October 2016 at the International School of Advanced Studies (SISSA) in Trieste, under the supervision of Markus Mueller and Antonello Scardicchio. After that, she moved to the Institut de Physique Theorique´ in Saclay, where she worked for two years as a PostDoc together with Giulio Biroli, within the Simons Collaboration on Cracking the Glass Problem.
She has a strong research interest in out-of-equilibrium dynamics, localization phenomena, random and complex landS scapes and glasses. Her main contribution to the field of quantum disordered systems is the investigation of integrability and emergent conservation laws in many-body systems that break ergodicity and do not equilibrate due to localization. More recently, she focused on the problem of characterizing the geometrical properties of rugged, high-dimensional landM scapes emerging in classical glassy and complex systems, with the goal of understanding how the landscapes structure affects their dynamical behaviour.
