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
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
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
Gregor JotzuGregor Jotzu is heading the Dynamic Quantum Materials Laboratory (DQML) at EPFL. His research concerns the control of quantum materials using intense low-frequency laser pulses and the dynamics of order formation in strongly correlated quantum systems.After reading physics at the University of Oxford, he became the 2009/2010 Michael von Clemm Fellow at Harvard University. He received his doctorate from ETH Zurich, where he studied quantum magnetism and driving-induced order using ultracold atoms in optical
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•
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
Matteo VilucchioEducation M.Sc. Theoretical Physics | 2020 Р2022 ENS Paris B.Sc. Physics | 2017 Р2020 Universit̩ de Pise
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
Konstantinos KarapiperisKostas Karapiperis is an Assistant Professor at EPFL directing the Data-Driven Mechanics Laboratory (LMD). His research lies at the intersection of mechanics, multiscale modeling, and data science, with applications ranging from geomaterials to structural materials. Prior to EPFL, he worked as a Postdoctoral Researcher and Lecturer at the Department of Mechanical and Process Engineering of ETH Zürich, supported by a Marie Sklodowska-Curie Fellowship. He holds a PhD in Applied Mechanics and a Minor in Applie
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 Zoë HolmesTeaching & PhD PhD Students Ricard Puig, Sacha Lerch, Reyhaneh Aghaei Saem, Shao Hen Chiew, Weijie Xiong, Marc Sanz Drudis, Mehrad Sahebi, Manuel Rudolph Courses Quantum information theory PHYS-550 After recapping the basics of quantum theory from an informationtheoretic perspective, we will cover more advanced topics inquantum information theory. This includes introducing measures of quantum information, and developing a more advanced understanding quantum states, channels and measurement
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
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
Michaela HirschmannTeaching & PhD PhD Students Patrick Hirling, Jonathan Petersson, Lucie Scharré Courses Astrophysics III : galaxy formation and evolution PHYS-465 Galaxy formation & evolution is about studying how galaxies in our Universe come into existence, how they evolve and what shapes their properties. This course describes the observational facts of galaxies and the various processes of galaxy evolution as seen from theoretical/numerical models. General physics : thermodynamics (English) P
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
