Sara BonellaEnseignement et PhD Doctorant·es actuel·les Edrick Solís González, Thibault Kläy, Paula Sierra i Varela, Federica Troni A dirigé les thèses EPFL de Jérémie Marc Bertrand, Taylor James Baird Cours Computational methods in molecular quantum mechanics CH-452 Le cours enseignerait les méthodes principales pour la simulation des propriétés quantique dependant du temps pour systèmes moléculaires. Les notions de base de density functional theory seront discutés. Effects quantiques nucleaires sero
Nikita KavokineEnseignement et PhD Doctorant·es actuel·les Lucas Bertrand Théo Floquet, Killian David Rigaux, Hao Lu, Peter Gispert, Léa Zupan Cours Mécanique statistique pour la chimie CH-242(b) Ce cours construit la base théorique universelle permettant de comprendre les systèmes à grand nombre de particules. Les méthodes introduites sont utilisées pour éclairer de nombreux phénomènes à travers la chimie. Nanofluidics CH-461 Ce cours introduit les méthodes théoriques modernes permettant
Clément HonglerMission
Quelques projets-clés:--La recherche de connexions entre la mécanique statistique et la théorie quantique des champs--Les réseaux de neurones, leur dynamique et leurs applications.--La vie artificielle, en particulier l'étude des automates cellulaires continus.--Les systèmes décentralisés.
2003-2006: BSc. EPFL, mathématiques2006-2008: MSc. EPFL, mathématiques2008-2010: Doctorat, Unige, mathématiques2010-2014: Ritt Assistant Professor, Columbia2014-2018: Professeur Assistant Tenure-Track, EPFL2019-pr
Sacha FriedliDiploma of Physics, Université de Lausanne, 1999
Phd, Institut de Physique Théorique, EPFL, 1999-2004
Post-doc, IMPA (Instituto Nacional de Matemática Pura e Aplicada), Rio de Janeiro, Brasil, 2004-2006
Adjunct professor, Departamento de Matemática (Universidade Federal de Minas Gerais), 2006-2016
Collaborateur Scientifique, Cours de Mathématiques Spéciales, EPFL, Lausanne, 2016-
Michael Christoph GastparMichael Gastpar is a (full) Professor at EPFL. From 2003 to 2011, he was a professor at the University of California at Berkeley, earning his tenure in 2008. He received his Dipl. El.-Ing. degree from ETH Zürich, Switzerland, in 1997 and his MS degree from the University of Illinois at Urbana-Champaign, IL, USA, in 1999. He defended his doctoral thesis at EPFL on Santa Claus day, 2002. He was also a (full) Professor at Delft University of Technology, The Netherlands. His research interests are in network information theory and related coding and signal processing techniques, with applications to sensor networks and neuroscience. He is a Fellow of the IEEE. He is the co-recipient of the 2013 Communications Society & Information Theory Society Joint Paper Award. He was an Information Theory Society Distinguished Lecturer (2009-2011). He won an ERC Starting Grant in 2010, an Okawa Foundation Research Grant in 2008, an NSF CAREER award in 2004, and the 2002 EPFL Best Thesis Award. He has served as an Associate Editor for Shannon Theory for the IEEE Transactions on Information Theory (2008-11), and as Technical Program Committee Co-Chair for the 2010 International Symposium on Information Theory, Austin, TX.
Karen ScrivenerDe nationalité anglaise, Karen Scrivener est née en 1958. Au cours de sa carrière, ses travaux et sa recherche traitaient des domaines suivants: Identification du développement microstucturale pendant l'hydratation du ciment. Elaboration d'une approche multitechnique pour étudier la microstucture des ciments et bétons, avec accent sur la quantification par analyse des images d'électrons retrodiffusés. Caractérisation de l'auréole de transition de la pâte de ciment autour des granulats. Compréhension des processus de dégardation des bétons, en particulier le gonflement lié à la formation de l'éttringite retardée dans les bétons étuvés.
Nicolas MacrisNicolas Macris received the PhD degree in theoretical physics from EPFL and then pursued his scientific activity at the mathematics department of Rutgers University (NJ, USA). He then joined the Faculty of Basic Science of EPFL, working in the field of quantum statistical mechanics and mathematical aspects of the quantum Hall effect. Since 2005 he is with the Communication Theories Laboratory and Information Processing group of the School of Communication and Computer Science and currently works at the interface of statistical mechanics, information theory and error correcting codes, inference and learning theory. He held long-term visiting appointments and collaborations with the University College and the Institute of Advanced studies in Dublin, the Ecole Normale Supérieure de Lyon, the Centre de Physique Theorique Luminy Marseille, Paris XI Orsay, the ETH Zürich and more recently Los Alamos National Lab. CV and publication list.
Lenka ZdeborováLenka Zdeborová is a Professor of Physics and of Computer Science in École Polytechnique Fédérale de Lausanne where she leads the Statistical Physics of Computation Laboratory. She received a PhD in physics from University Paris-Sud and from Charles University in Prague in 2008. She spent two years in the Los Alamos National Laboratory as the Director's Postdoctoral Fellow. Between 2010 and 2020 she was a researcher at CNRS working in the Institute of Theoretical Physics in CEA Saclay, France. In 2014, she was awarded the CNRS bronze medal, in 2016 Philippe Meyer prize in theoretical physics and an ERC Starting Grant, in 2018 the Irène Joliot-Curie prize, in 2021 the Gibbs lectureship of AMS. She is an editorial board member for Journal of Physics A, Physical Review E, Physical Review X, SIMODS, Machine Learning: Science and Technology, and Information and Inference. Lenka's expertise is in applications of concepts from statistical physics, such as advanced mean field methods, replica method and related message-passing algorithms, to problems in machine learning, signal processing, inference and optimization. She enjoys erasing the boundaries between theoretical physics, mathematics and computer science.
Vittorio ErbaEnseignement et PhD Cours Statistical physics of computation PHYS-512 Les étudiants comprennent les outils de la physique statistique des systèmes désordonnés et les appliquent pour étudier des problèmes d'informatique et de statistiques en théorie des graphes, en optimisation discrète, ainsi qu'en inférence et en apprentissage automatique.
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