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 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.
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
Rajita Ramesh ChandakExpertise
Nonparametric estimation, statistical inference, theory of machine learning
Teaching & PhD Courses Nonparametric estimation and inference MATH-524 Nonparametric models are used to identify a wide range of relationships within data. This course gives a graduate-level overview of nonparametric statistical estimation and inference theory. Statistical inference MATH-562 Inference from the particular to the general based on probability models is central to the statistica
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).
Laura GrigoriExpertise
Numerical linear algebra, deterministic and randomized algorithmsHigh performance computing for scientific applicationsHigh dimensional problems and tensor computationsApplications in astrophysics, molecular simulations, etcCombinatorial scientific computingMathematical Software
Teaching & PhD PhD Students Israa Fakih, Zhipeng Xue, Andrea Di Antonio, Mariana Graciela Martinez Aguilar, Guifré Sánchez i Serra Courses Numerical analysis and computational mathematics MATH-456 The cou
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
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
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