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
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
Keivan Faghih NiresiExpertise
Computational sensing/imaging, Inverse problems, Signal processing, Graph neural networks, Domain adaptation.
Keivan is a PhD student at école Polytechnique Fédérale de Lausanne (EPFL). He joined the Intelligent Maintenance and Operations Systems (IMOS) Lab under the supervision of Prof. Olga Fink in February 2023.Prior to joining EPFL, he obtained his master's degree from the Institute of Communications Engineering, College of Electrical Engineering and Computer Science, National Tsing Hua Univer
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.
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.
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).
Mats Julius StensrudTeaching & PhD PhD Students Ignacio Gonzalez Perez, Gellért Géza Perényi, Lorenzo Gasparollo, Julien David Laurendeau Past EPFL PhD Students Matias Janvin, Amit Sawant Courses Causal thinking MATH-352 This course will give a unified presentation of modern methods for causal inference. We focus on concepts, and we will present examples and ideas from various scientific disciplines, including medicine, computer science, engineering, economics and epidemiology. Probability and statistics (
Adriano MeleI am a control engineer and plasma scientist at the Swiss Plasma Center of the École Polytechnique Fédérale de Lausanne.I am also Research Topic Coordinator in the EUROfusion Tokamak Exploitation WorkPackage, for the Research Topic 04: Physics-based machine generic systems for an integrated control of plasma discharge, and an Associate Editor in the Franklin Open journal.In 2022-2023, I have been an Assistant Professor in Automatic Control at the University of Tuscia, Viterbo.Before that, from 2019 to 2021,
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•
Benjamin Alexander Kellenberger2020–: Postdoctoral Researcher, EPFL, Sion, Switzerland2020: Postdoctoral researcher, Wageningen University, Netherlands2017–2020: PhD (cont'd); Wageningen University, Netherlands2016–2017: PhD; University of Zurich, Switzerland2009–2014: BSc. and MSc. in geography (remote sensing and GIS) and computer science; University of Zurich, Switzerland
Du WenEducation PhD | Doctor of Philosophy, Mechanical Engineering 2020 – 2023 The University of Tokyo MEng | Master of Engineering, Power Engineering and Engineering Thermophysics 2016 – 2019 Xi’an Jiaotong University Bsc | Bachelor of Engineering 2012 – 2016 Xi’an Jiaotong University
Alessandro ChiesaAlessandro Chiesa is a faculty member in computer science. He conducts research in complexity theory, cryptography, and security, with a focus on the theoretical foundations and practical implementations of cryptographic proofs that are short and easy to verify. He is a co-author of several zkSNARK libraries, and is a co-inventor of the Zerocash protocol. He has co-founded Zcash and StarkWare Industries. He is a recipient of a Sloan Research Fellowship (2021), an Okawa Foundation Research Grant (2020), and Google Faculty Research Awards (2018 and 2017). He was included in MIT Technology Review's "35 Innovators Under 35" list in 2018.
Nicolas Henri Bernard FlammarionNicolas Flammarion is a tenure-track assistant professor in computer science at EPFL. Prior to that, he was a postdoctoral fellow at UC Berkeley, hosted by Michael I. Jordan. He received his PhD in 2017 from Ecole Normale Superieure in Paris, where he was advised by Alexandre d’Aspremont and Francis Bach. In 2018 he received the prize of the Fondation Mathematique Jacques Hadamard for the best PhD thesis in the field of optimization. His research focuses primarily on learning problems at the interface of machine learning, statistics and optimization.
Alexander DittrichCurrent Work
My research interests are somewhere between neural networks with Hebbian plasticity, meta-reinforcement learning, and the control of rather unconventional soft robotic platforms like tensegrity robots.
Michail VlachosTeaching & PhD Courses Data science and machine learning MGT-502 Hands-on introduction to data science and machine learning. We explore recommender systems, generative AI, chatbots, graphs, as well as regression, classification, clustering, dimensionality reduction, text analytics, neural networks. The course consists of lectures and coding sessions using Python.
