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.
Olga FinkTeaching & PhD PhD Students Han Sun, Leandro Von Krannichfeldt, Keivan Faghih Niresi, Zepeng Zhang, Vinay Sharma, Chenghao Xu, Raffael Pascal Theiler, Kevin Antonio Steiner, Sergei Garmaev, Amaury Wei Past EPFL PhD Students Ismail Nejjar, Mengjie Zhao Courses Data Science for infrastructure condition monitoring CIVIL-332 The course will cover the relevant steps of data-driven infrastructure condition monitoring, starting from data acquisition, going through the steps pre-processing of real
Vittorio ErbaTeaching & PhD Courses Statistical physics of computation PHYS-512 The students understand tools from the statistical physics of disordered systems, and apply them to study computational and statistical problems in graph theory, discrete optimisation, inference and machine learning.
Giuseppe CarleoGiuseppe Carleo is a computational quantum physicist, whose main focus is the development of advanced numerical algorithms tostudy challenging problems involving strongly interacting quantum systems.He is best known for the introduction of machine learning techniques to study both equilibrium and dynamical properties,based on a neural-network representations of quantum states, as well for the time-dependent variational Monte Carlo method.He earned a Ph.D. in Condensed Matter Theory from the International School for Advanced Studies (SISSA) in Italy in 2011.He held postdoctoral positions at the Institut d’Optique in France and ETH Zurich in Switzerland, where he alsoserved as a lecturer in computational quantum physics.In 2018, he joined the Flatiron Institute in New York City in 2018 at the Center for Computational Quantum Physics (CCQ), working as a Research Scientist and project leader, and also leading the development of the open-source project NetKet.Since September 2020 he is an assistant professor at EPFL, in Switzerland, leading the Computational Quantum Science Laboratory (CQSL).
Virginia CarnevaliExpertise
Theoretical condensed matter physics: theory and simulations of structural, electronic, magnetic and optical properties (Quantum ESPRESSO), ab-initio molecular dynamics (CP2K), and Kinetic Monte Carlo codes; automate Quantum ESPRESSO workflows and high-throughput calculations (AFLOWÏ€); generation of tight-binding hamiltonians, transport properties, and data analysis (PAOFLOW).Materials for photovoltaic: Pb- and Sn-based organic perovskites; investigation and improvement of the HTL-ETL interaction
Anne-Florence Raphaëlle BitbolI was trained in physics, and always had a strong interest in biology. I am broadly interested in understanding biological phenomena in a quantitative way, through physical concepts and mathematical and computational tools. I investigate the relative impacts of optimization and historical contingency in biological evolution, both at the scale of protein sequences and at the scale of microbial populations.Here is my research group website.Curriculum vitae Here is my complete CV. Professional experience:Since
Myrto LimniosTeaching & PhD Courses Empirical processes MATH-522 We learn how to control the nonasymptotic and random behavior of collections of estimators, when indexed by classes of functions/sets. Examples range from prototypical estimators used by statisticians, to nonparametric models used in machine learning. Regression methods MATH-408 General graduate course on regression methods
Geovani RizkExpertise
Distributed Machine Learning, Robustness, Safety, Optimization, Multi-Agent, Bandits in Graphs
I am a postdoctoral researcher at école Polytechnique Fédérale de Lausanne since 2022, where I work with Rachid Guerraoui on robustness of distributed machine learning algorithms. From 2018 to 2022, I completed my Ph.D. in computer science at Université Paris Dauphine-PSL on multi-agent bandits in graphs under the supervision of Yann Chevaleyre, Rida Laraki, Igor Colin, and Albert Thomas.Pre-prints Over
Michael HerbstExpertise
For more information about our research, see https://matmat.org/research/.
Teaching & PhD PhD Students Niklas Frederik Schmitz, Anna Paulish, Bruno Ploumhans, Benedikt Menges Courses Error control in scientific modelling MATH-500 Errors are ubiquitous in computational science as neither models nor numerical techniques are perfect. With respect to eigenvalue problems motivated from materials science and atomistic modelling we discuss, implement and apply numerical techniques for e Lénaïc ChizatTeaching & PhD PhD Students Javier Esteban Maass Martinez Past EPFL PhD Students Guillaume Yitian Wang Courses Computational optimal transport MATH-528 Computational aspects of measure transportation, from classical optimal transport to modern denoising diffusion models. Topics in machine learning MATH-520 Mathematical analysis of modern supervised machine learning techniques, with an emphasis on the mathematics of artificial neural networks.
Daryl Yee Wei LiangIncoming Assistant Professor of Electrical and Microengineering (March 2023)
Martin SchrimpfExpertise
NeuroAI, computational neuroscience, computer vision, natural language processing, human alignment, startups [CV]
Mission
Modeling the brain: Martin's research focuses on a computational understanding of the neural mechanisms underlying natural intelligence in vision and language. To achieve this goal, he bridges Deep Learning, Neuroscience, and Cognitive Science, building artificial neural network models that match the brain’s neural representations in their internal processing and are aligned to
Aleksandar AntanasijevicTeaching & PhD PhD Students Camilla Di Giulio, Kiruthika Kumar, Yash Garodia Courses Biological chemistry I BIO-212 Biochemistry is a key discipline for the Life Sciences. Biological Chemistry I and II are two tightly interconnected courses that aim to describe and understand in molecular terms the processes that make life possible. General chemistry for SV CH-100 This course covers essential notions in general chemistry, relative to matter and its structure, equilibriums
Giovanna LaniExpertise
Theory development within many-body perturbation theory, time-dependent density functional theory and density-functional theory for problems pertaining to condensed matter physics and quantum chemistry
Expertise
Theory development within many-body perturbation theory, time-dependent density functional theory and density-functional theory for problems pertaining to condensed matter physics and quantum chemistry
Sanggyu ChongSanggyu "Raymond" Chong obtained his BS, MS and PhD degrees in Chemical and Biomolecular Engineering from Korea Advanced Institute of Science and Technology (KAIST), where he primarily focused on the computational design of metal-organic frameworks for applications ranging from gas storage to functional electronics. In March 2022, Sanggyu joined COSMO as a postdoctoral researcher, and he is currently investigating the robustness of local and/or component-wise predictions made by atomistic machine learning m
Pasquale ScarlinoI obtained my master's degree in Physics at the University of Salento, Lecce (Italy) in February 2011. During 2006-2011, I have also been a student of Scuola Superiore ISUFI (SSI). SSI is one of six schools of excellence established in Italy to develop the intellectual capital in technological and social sciences. I conducted an external Master thesis project during an 8 months internship in the Quantum Transport Group at TU Delft, under the supervision of Prof. L.M.K. Vandersypen. There, I implemented the Quantum Point Contact Radio-Frequency Reflectometry technique, which allows increasing the single-shot electron spin readout bandwidth and is currently routinely used in the group.I obtained my Ph.D. degree in February 2016, in the Spin Qubits group of Prof. L.M.K. Vandersypen at the Kavli Institute of Nanoscience-Qutech (TU Delft). During my Ph.D. I have been leading the Si/SiGe spin qubits project, collaborating with the M. Eriksson Group at Wisconsin University. In parallel, I have been working on other different projects, in particular with GaAs depletion quantum dots, high impedance superconducting resonators, and surface acoustic wave resonators. I have been working as a Postdoc fellow in the group of Prof. A. Wallraff (Quantum Device Lab) at ETH Zurich. My main project, in collaboration with the group of Prof. K. Ensslin and Prof. T. Ihn, consisted in integrating semiconductor and superconductor technologies. Realizing a well-controlled interface between the semiconductor and superconductor-based quantum information technologies may allow harnessing the best of both device architectures, for example by providing an interface between strongly coupled charge state and high coherence spin states. Furthermore, it enables the possibility to explore light/matter hybridization in a class of solid-state systems and regimes that are new in the context of quantum optics.From June 2019 till September 2020, I have been a Senior Researcher at Microsoft Station Q Copenhagen and at the Center for Quantum Devices in Copenhagen, focusing on developing semiconductor-superconducting hybrid hardware for topologically protected quantum computation.Since October 2020, I am a tenure track Assistant Professor of Physics in the School of Basic Sciences at the EPFL where I founded the Hybrid Quantum Circuit (HQC) laboratory.
