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
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
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
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.
Fides ZenkTeaching & PhD PhD Students Hanrong Hu, Eugenio Stefano Gentile, Ece Naz Bilgiç, Nahuel Jonas Simonet, Yong-Qi Gao, Tekin Can Sobaci, Angel Lizandro Polanco Maldonado Courses Neuroscience foundations for engineers BIOENG-310 This overview course bridges computational expertise with neuroscience fundamentals, aimed at fosteringinterdisciplinary communication and collaboration for engineering-based neuroscience programs. Practical - Zenk Lab BIO-625 You will learn to prepare
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.
Ayberk OzkirliDr. Ayberk Ozkirli is a vision neuroscientist whose research investigates how spatiotemporal context influences visual perception.He completed his PhD in Neuroscience at the Laboratory of Psychophysics at EPFL in 2025, under the supervision of Prof. Michael Herzog and Prof. David Pascucci. He holds an M.Sc. in Bioengineering from EPFL (Switzerland) and a B.Sc. in Electrical and Electronics Engineering from Bilkent University (Turkey).His expertise spans neuroimaging, statistics, experimental design, neural
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.
Radoslav MarchevskiTeaching & PhD PhD Students Jou An Chen, Tobias Alexandre Monnard, Xiafei Chang, Gauri Napoletano, Kerim Guseinov, Noémie Duc Courses Particle physics I PHYS-415 Presentation of particle properties, their symmetries and interactions.Introduction to quantum electrodynamics and to the Feynman rules. Particle physics: the flavour frontiers PHYS-471 This course will present experimental aspects of flavour physics primarily in the quark sector but also in the lepton sector and
Brayden Graham MyersTeaching & PhD Courses General physics : mechanics (English) PHYS-101(en) Students will learn the principles of mechanics to enable a better understanding of physical phenomena, such as the kinematics and dyamics of point masses and solid bodies. Students will acquire the capacity to quantitatively analyze these effects with the appropriate theoretical tools.
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.
Nicolas ThomäTeaching & PhD PhD Students David Domjan, Pierre Alexander Miranda Herrera, Laurin Tim Kanis, Alessandro Minafra, Pau Reolid Coll Courses Practical - Thomä Lab BIO-624 Expression and purification of recombinant proteins are key methods in our lab studying protein machines on a structural level and ways in which they can be reprogrammed by small molecules. In the course, we will express and purify proteins and biophysically characterise them. Trends in chemical biology and drug discovery
Elisabeth AgoritsasExpertise
=> See my personal homepage for more content https://eagoritsas.wordpress.com.Statistical physics of classical disordered systems, with focus on specific classes of models:disordered elastic interfaces and random manifolds,mesoscopic elasto-plastic models (spatially-resolved and effectively mean-field versions),mean-field description of structurally disordered systems (e.g. glasses) in infinite dimensions,continuous random perceptron model.1D Kardar-Parisi-Zhang equation and its encompassing unive 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
Alexander MathisAlexander studied pure mathematics with a minor in logic and theory of science at the Ludwig Maximilians University in Munich. For his PhD also at LMU, he worked on optimal coding approaches to elucidate the properties of grid cells. As a postdoctoral fellow with Prof. Venkatesh N. Murthy at Harvard University and Prof. Matthias Bethge at Tuebingen AI, he decided to study olfactory behaviors such as odor-guided navigation, social behaviors and the cocktail party problem in mice. During this time, he increasingly got interested sensorimotor behaviors beyond olfaction and started working on proprioception, motor adaption, as well as computer vision tools for measuring animal behavior.
In his group, he is interested in elucidating how the brain gives rise to adaptive behavior. One of the major goals is to synthesize large datasets into computationally useful information. For those purposes, he develops algorithms and systems to analyze animal behavior (e.g. DeepLabCut), neural data, as well as creates experimentally testable computational models.
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.
Christoph BostedtTeaching & PhD PhD Students Eloisa Florence Manetti, Wenyi Jiang, Hankai Zhang, Loïc Lucien Cédric Bassement, Silas Strebel, Eliot Jermann Past EPFL PhD Students Jonas Knurr, Ningchen Yang Courses Optical methods in chemistry CH-417 Introduction and application of photon based tools for chemical sciences: from basic concepts to optical and x-ray lasers Physical Chemistry Seminar 1: Trends CH-643 Students can learn about the broader research in Phyiscal Chemistry and broaden
