Sofia Charlotta OlhedeSofia Olhede is a professor of Statistics at EPFL in Switzerland. She joined UCL prior to this in 2007, before which she was a senior lecturer of statistics (associate professor) at Imperial College London (2006-2007), a lecturer of statistics (assistant professor) (2002-2006), where she also completed her PhD in 2003 and MSci in 2000. She has held three research fellowships while at UCL: UK Engineering and Physical Sciences Springboard fellowship as well as a five-year Leadership fellowship, and now holds a European Research Council Consolidator fellowship. Sofia has contributed to the study of stochastic processes; time series, random fields and networks. Sofia was part of the multi-institutional team that set up the UK national data science institute, the Alan Turing Institute. She organised and served as chair of the science committee that developed the initial 500 000 pounds scientific programme of the institute; peer-reviewing over 100 workshop proposals and hosting over 30. She also chaired the first recruitment wave of the institute hiring 13 data scientists as a multi-university recruitment drive. Sofia was a member of the Royal Society and British Academy Data Governance Working Group, and the Royal Society working group on machine learning. Most recently she was one of 3 commissioners on a law society commission on the usage of algorithms in the justice system.
Giancarlo Ferrari TrecateExpertise
Automatic control, Dependable control, Neural-network Control, Machine learning, Microgrids, Hybrid systems
Mission
See the webpage of the DECODE (Dependable Control and Decision) group.
Teaching & PhD PhD Students Laura Meroi, Julien Pallage, Nicolas Léo Pierre Kirsch, Simone Baratto, Daniele Martinelli, Riccardo Cescon, Jakob Nylöf, Sabri El Amrani Past EPFL PhD Students Pulkit Nahata, Mustafa Sahin Turan, Baiwei Guo, Jean-Sébastien Brouillon, Clara Galimberti, Andrea Martin, Muhammad Zakwan,
Ayberk OzkirliExpertise
Magnetic resonance imaging (MRI), visual crowding, serial dependence, population receptive field (pRF) mapping, data analysis, machine learning, age-related macular degeneration, neuroengineering, clinical neuroscience
Expertise
Magnetic resonance imaging (MRI), visual crowding, serial dependence, population receptive field (pRF) mapping, data analysis, machine learning, age-related macular degeneration, neuroengineering, clinical neuroscience
Martin JaggiMartin Jaggi is a Tenure Track Assistant Professor at EPFL, heading the Machine Learning and Optimization Laboratory. Before that, he was a post-doctoral researcher at ETH Zurich, at the Simons Institute in Berkeley, and at École Polytechnique in Paris. He has earned his PhD in Machine Learning and Optimization from ETH Zurich in 2011, and a MSc in Mathematics also from ETH Zurich.
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.
Olga FinkEnseignement et PhD Doctorant·es actuel·les Han Sun, Leandro Von Krannichfeldt, Keivan Faghih Niresi, Zepeng Zhang, Vinay Sharma, Chenghao Xu, Raffael Pascal Theiler, Kevin Antonio Steiner, Sergei Garmaev, Amaury Wei A dirigé les thèses EPFL de Ismail Nejjar, Mengjie Zhao Cours Data Science for infrastructure condition monitoring CIVIL-332 Le cours couvrira les étapes pertinentes de la surveillance de l'état des infrastructures basée sur les données: l'acquisition des données, en passant p
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
Vittorio ErbaEnseignement et PhD Cours Statistical physics of computation PHYS-512 Les étudiants comprennent les outils de la physique statistique des systèmes désordonnés et les appliquent pour étudier des problèmes d'informatique et de statistiques en théorie des graphes, en optimisation discrète, ainsi qu'en inférence et en apprentissage automatique.
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
Younes Pierre ZineExpertise
I mainly work on (deterministic and random) nonlinear PDEs by using techniques and insights from PDE theory, probability theory and Fourier analysis.My website:https://younes-zine.github.io
Expertise
I mainly work on (deterministic and random) nonlinear PDEs by using techniques and insights from PDE theory, probability theory and Fourier analysis.My website:https://younes-zine.github.io
Education Ph.D. | 2020 – 2023 The University of Edinburgh Oliver JanzerEnseignement et PhD Doctorant·es actuel·les Rik Sarkar Cours Algebraic methods in combinatorics MATH-526 Dans ce cours, nous étudions comment les méthodes algébriques peuvent être utilisées pour résoudre des problèmes de combinatoire. Les principaux outils utilisés proviennent de l'algèbre linéaire et de la théorie des polynômes. Graph theory MATH-360 Le cours introduit les concepts de base et certains résultats de la théorie moderne des graphes.
Friedhelm Christoph HummelExpertise
Stroke recoveryHealthy AgingMotor ControlMotor LearningCognitive ControlNeuroplasticityNon-invasive Brain StimulationStructural and functional NeuroimagingConnectivity
Enseignement et PhD Doctorant·es actuel·les Maria Ploumitsakou, Simona Losacco, Fabienne Windel, Andrea Protani, Stavroula Skarvelaki, Wei Chung Hsu, Rebecca Jane Jones, Beatrice Lugli, Thomas Paul, Paul de Fontenay, Michele Di Ponzio, Léa Kim-mi Ho Dac, Dong Jin Sung, Andrea Nuti, Greta Carnevali A dirigé les thèses EPFL de Rober
Robert WestRobert West is a tenure-track assistant professor of computer science at EPFL, where he heads the Data Science Lab. In his research, he develops and applies techniques in machine learning, computational social science, natural language processing, social network analysis, and data mining. Bob also collaborates closely with the Wikimedia Foundation, in his role as a Wikimedia Research Fellow. Bob’s work has won several awards, including best/outstanding paper awards at ICWSM’21, ICWSM’19, and WWW’13, a best-paper runner-up award at WWW’16, a Google Faculty Research Award, a Facebook Research Award, a Hewlett-Packard Graduate Fellowship, and a Facebook Graduate Fellowship. He is actively involved in the research community, e.g., as an Associate Editor of ICWSM and EPJ Data Science and as a co-founder of the Wiki Workshop (held at WWW and ICWSM) and the Applied Machine Learning Days. Bob received his PhD in Computer Science from Stanford University, his MSc from McGill University, Canada, and his undergraduate degree from Technische Universität München, Germany.[Last updated: 25 Aug 2021]
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
Jonathan Yuelin DongEnseignement et PhD Doctorant·es actuel·les Zhiyuan Hu A co-dirigé les thèses EPFL de Yan Liu
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
Gioele La MannoEnseignement et PhD Doctorant·es actuel·les Albert Dominguez Mantes, Xinyi Huang, Laura Lapeña Gil, Alessandro Valente, Luca Fusar Bassini, Hao-An Hsiung, Alireza Gargoorimotlagh, Morgane Alice Storey, Rosan Martine Hans, María Cristina Loureiro Casalderrey, Lada Polyakova A dirigé les thèses EPFL de Alex Russell Lederer, Irina Khven A co-dirigé les thèses EPFL de Alexandre Coudray, Alex Russell Lederer, Halima Hannah Emilie Schede, Irina Khven Cours Biological data science I: statistical learning
Martin Alois RohrmeierEnseignement et PhD Doctorant·es actuel·les Joris Monnet, Marina Borsodi-Benson, Xinyi Guan, Xiaoxuan Wang, Zeng Ren A dirigé les thèses EPFL de Fabian Claude Moss, Daniel Harasim, Christoph Finkensiep, Johannes Hentschel, Gabriele Cecchetti, Shuxin Meng Cours Digital musicology DH-401 Ce cours a pour but de familiariser les étudiants avec les thèmes principaux en musicologie digitale, ainsi que les approches et méthodes théoriques essentielles. Dans la partie pratique, les étudiants feron
Pablo Antolin SanchezEnseignement et PhD Doctorant·es actuel·les Philipp Christoph Weder, Davor Dobrota A co-dirigé les thèses EPFL de Ondine Chanon Cours Analysis III MATH-202(c) Le cours étudie les concepts fondamentaux de l'analyse vectorielle et de l'analyse de Fourier-Laplace en vue de leur utilisation pour résoudre des problèmes pluridisciplinaires d'ingénierie scientifique. Analysis III (for IC) MATH-203(d) Le cours étudie les concepts fondamentaux de l'analyse vectorielle et de l'analys
