Maria PavlouEnseignement et PhD Doctorant·es actuel·les Yong-Qi Gao Cours Methods: omics in biomedical research BIOENG-519 Les méthodologies à haut débit, appelées "Omiques", permettent de caractériser la complexité et la dynamique de tout système biologique. Ce cours fournira une description générale des méthodes liées au domaine des "Omiques", suivie de rotations pratiques dans les plateformes participantes.
Mika Tapani GöösEnseignement et PhD Doctorant·es actuel·les Valentin Imbach, Ziyi Guan, Artur Riazanov, Anastasiia Sofronova, Weiqiang Yuan A dirigé les thèses EPFL de Maystre Gilbert Théodore Cours Computational complexity CS-524 En théorie de la complexité, nous étudions les ressources nécessaires à résoudre des problèmes et les relations entre différents modèles de calcul.Ce cours donne une meilleure connaissance de la complexité informatique et aborde les questions ouvertes fondamentales.
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.
Martin JaggiMartin Jaggi is a Tenure Track Assistant Professor at EPFL, heading the Machine Learning and Optimization Laboratory. Before that, he was a post-doctoral researcher at ETH Zurich, at the Simons Institute in Berkeley, and at École Polytechnique in Paris. He has earned his PhD in Machine Learning and Optimization from ETH Zurich in 2011, and a MSc in Mathematics also from ETH Zurich.
Nako NakatsukaMission
At the Laboratory of Chemical Nanotechnology (CHEMINA), our mission is to develop translational chemical biosensing technologies that advance human health. Working at the intersection of chemistry, engineering, and neuroscience, we seek to probe the dynamic chemical landscape of biological systems across in vitro systems, in vivo models, and human clinical samples. We develop novel biorecognition elements and innovate biosensing platforms to enable monitoring of small molecules in complex biofluids,
Simon Elias BibriExpertise
• Sustainable cities• Smarter eco-cities• Sustainable smart cities• Artificial Intelligence of Things (AIoT) • Generative Artificial Intelligence of Things (GAIoT)• Foundation Models (FMs) • Quantum Generative Artificial Intelligence (QGAI)• Urban Digital Twins (UDTs)• Urban Brain (UB) • Smart Circular Metabolism• Urban planning and design• Environmental sustainability and climate resilience• Sustainable smart energy• Sustainable smart transportation• Sustainable smart waste and water management•
Lénaïc ChizatEnseignement et PhD Doctorant·es actuel·les Javier Esteban Maass Martinez A dirigé les thèses EPFL de Guillaume Yitian Wang Cours Computational optimal transport MATH-528 Aspects computationnels de la théorie du transport de mesures, du problème classique du transport optimal classique au modèles de diffusion modernes. Topics in machine learning MATH-520 Analyse mathématique des techniques modernes d'apprentissage supervisé, avec une emphase sur les réseaux de neurones arti
Patrick Daniel BarthProfessor Patrick Barth is Associate Professor at EPFL and Adjunct Associate Professor at Baylor College of Medicine, Houston, TX, USA. He received training in Physics, Chemistry and Biology (University of Paris, ENS) in France and performed his PhD at the Commissiariat a l'Energie Atomique in Saclay, France on structure/function studies of membrane proteins (photosystem I) using biochemical and biophysical experimental techniques. He carried out postdoctoral studies at University of California at Berkeley with Tom Alber on computational development for calculating protein electrostatics and designing de novo selective peptide inhibitors of cellular protein interactions. He then went to the University of Washington as a postdoctoral fellow and instructor in David Baker's laboratory to develop computational techniques in the software Rosetta for predicting and designing membrane protein structures. He started his independent career and received tenure at Baylor College of Medicine. He will continue at EPFL to marry computation and experiment for understanding the molecular determinants of signal transduction, as well as modeling and designing membrane proteins with novel functions for various synthetic biology and therapeutic applications.
