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
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 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
Zepeng ZhangZepeng is a PhD student at École Polytechnique Fédérale de Lausanne (EPFL). He works in the Intelligent Maintenance and Operations Systems (IMOS) Lab under the supervision of Prof. Olga Fink. Previously, He received his master’s degree at ShanghaiTech University and B.Eng. degree from Wuhan University (WHU). During his studies, he have visited Télécom Paris, Institut Polytechnique de Paris (IP Paris), Peking University (PKU), and City University of HongKong (CityU) as a research intern. His current research
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.
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.
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
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
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
Oliver JanzerTeaching & PhD PhD Students Rik Sarkar Courses Algebraic methods in combinatorics MATH-526 In this course we study how algebraic methods can be used to solve problems in combinatorics. The main tools used are from linear algebra and from the theory of polynomials. Graph theory MATH-360 The course aims to introduce the basic concepts and results of modern Graph Theory.
Awards European Prize in Combinatorics 2025 British Combinatorial Committee PhD Prize 2022 Smith-Knight
Francesca RodinoExpertise
Electrochemical BiosensorsSensors and Biosensors interfaces | front-end electronicsBiomedical circuits and systems
Francesca Rodino received the B.Sc. and M.Sc. degrees in Biomedical Engineering from the Politecnico di Torino, Turin, Italy, in 2019 and 2022, respectively. She developed her Master's project in the field of cortical neuroprosthesis devices for artificial vision in collaboration with the Integrated Circuits Laboratory (ICLAB) at école Polytechnique Fédérale de Lausanne (EPFL) in Swit
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
Mengjie ZhaoMengjie Zhao holds degrees in Computational Mechanics (MSc) with honors track and in Engineering Science (BSc) from the Technical University of Munich (TUM).
From the early years of her studies, Mengjie was fascinated by the modeling of multiphysics and multiscale systems. As a student research assistant at TUM and research intern at International Centre for Numerical Methods in Engineering (CIMNE), she gained a solid understanding of both the theoretical and algorithmic fundamentals as well as a wide range of applications. Through the BGCE project with the Elitenetzwerk Bayern (ENB), which dealt with the mesh sensitivity prediction with a deep neural network, she realized that leveraging data could bring physical modeling far beyond the current computational limits. Later, in her master's thesis in cooperation with Siemens, she turned to the reduced-order modeling with enforced physical invariants, which showed better accuracy and generality.
In her Ph.D., she would like to step towards a further combination of deductive research (modeling and simulation) and inductive (data-driven) research by embedding physics into machine learning.
