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
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.
Mats Julius StensrudEnseignement et PhD Doctorant·es actuel·les Ignacio Gonzalez Perez, Gellért Géza Perényi, Lorenzo Gasparollo, Julien David Laurendeau A dirigé les thèses EPFL de Matias Janvin, Amit Sawant Cours Causal thinking MATH-352 Ce cours donnera une présentation unifiée des méthodes modernes d'inférence causale. Nous nous concentrons sur les concepts, et nous présenterons des exemples et des idées provenant de diverses disciplines scientifiques, notamment la médecine, l'informatique, l'ingénierie,
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
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
Myrto LimniosExpertise
Nonparametric statistics, statistical learning theory and methods, stochastic processes
Enseignement et PhD Cours Empirical processes MATH-522 Nous étudierons comment contrôler le comportement non-asymptotique et aléatoire de collections d'estimateurs, lorsqu'indexées par des classes de fonctions/d'ensembles. Des exemples en statistiques et des méthodes non-paramétriques en apprentissage automatique seront traités. Regression methods MATH-408 Cours de niveau master
Andrew James SontaExpertise
Human-building interaction, building energy, occupant behavior, sustainable design and planning, walkability, network analysis, machine learning, urban informatics, social systems.
Andrew Sonta is a tenure track assistant professor of sustainable civil engineering at EPFL. He directs the ETHOS Lab: Engineering and Technology for Human Oriented Sustainability. ETHOS is an interdisciplinary research group focused on using data-driven and computational tools to create interventions in the built envir
Mackenzie MathisCenter for NeuroprostheticsEPFL ELLIS Unit Faculty MemberCenter for Intelligent Systems
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.
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
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.
Lénaïc ChizatEnseignement et PhD Doctorant·es actuel·les Javier Esteban Maass Martinez A dirigé les thèses EPFL de Guillaume Yitian Wang Cours Computational optimal transport MATH-528 Aspects computationnels de la théorie du transport de mesures, du problème classique du transport optimal classique au modèles de diffusion modernes. Topics in machine learning MATH-520 Analyse mathématique des techniques modernes d'apprentissage supervisé, avec une emphase sur les réseaux de neurones arti
Emma Elizabeth TolleyI studied the universe at the smallest scales at the Large Hadron Collider, now we're getting ready to study it at the largest scales with the Square Kilometer Array. My research focuses on developing high-performance computing and new data science techniques to image and analyse the radio sky.
Enseignement et PhD Doctorant·es actuel·les Markus Johan Bredberg, Ashutosh Kumar Mishra A co-dirigé les thèses EPFL de Shreyam Parth Krishna Cours Physique générale : mécanique (classe inversée en anglais)
Baraka Jean-Claude MunyakaExpertise
Transport modelling, Logistics, Supply Chain Systems, and Urban Planning, Geospatial Analysis, Lean Manufacturing
Dr. Jean-Claude Munyaka Baraka is a Research Collaborator at the Urban and Regional Planning Community (CEAT) at the Swiss Federal Institute of Technology in Lausanne (EPFL). A citizen of the Democratic Republic of Congo, Dr. Munyaka holds a Doctorate in Industrial and Systems Engineering from the University of Pretoria, South Africa, and earned his Master's degree with Cum Laude in th
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
Andreas FusterAndreas Fuster is an Associate Professor of Finance at Swiss Finance Institute @ EPFL and a Research Fellow at the CEPR. Previously, he worked at the Federal Reserve Bank of New York and the Swiss National Bank. Andreas's main research interests are in empirical finance, macroeconomics, and behavioral economics. His recent work has focused in particular on the effects of technological advances on household credit markets. Andreas’s research has been published in academic journals such as the Quarterly Journal of Economics, the Review of Economic Studies, the Journal of Finance, and the Review of Financial Studies. Andreas obtained his Ph.D. from Harvard University and also holds an M.Phil. from Oxford University and a B.A. from the University of Lausanne (Switzerland), all in economics.
Emmanouil BarmpounakisI am a Postdoctoral Researcher in the Urban Transport Systems Laboratory (LUTS) of the École Polytechnique Fédérale de Lausanne (EPFL). I hold a Doctoral Degree in Transportation Engineering from the National Technical University of Athens having graduated in 2017 and a diploma in Civil Engineering (Transportation Engineering Cycle) having graduated in 2012.My primary research field is Traffic Operations, Unmanned Aerial Systems (UAS) for traffic operations and Data Science. My professional experience includes participation in projects and traffic studies in Greece.
Tomas Teijeiro CampoI received my PhD from the Centro Singular de Investigación en Tecnoloxías Intelixentes (CITIUS), University of Santiago de Compostela, Spain, in 2017. During my doctoral studies I developed a novel knowledge-based framework for time series interpretation based on abductive reasoning that has been successfully applied to automatic ECG interpretation and classification. Now I am currently working as a research associate at the Embedded Systems Laboratory (ESL), with Prof. David Atienza. My research interests include knowledge representation, non-monotonic temporal reasoning, event-based sensing, and their application to biosignal abstraction and interpretation in energy-efficient setups.
Aengus Killian CollinsAengus Collins works primarily on the policy challenges posed by emerging technologies and on concrete applications of the IRGC’s risk governance framework. He joined the IRGC in May 2019 from the World Economic Forum, where as Head of Global Risks he was lead author of the annual Global Risks Report. Previously, he worked as Country Forecast Director at the Economist Intelligence Unit in London, focusing in particular on political and economic risks in the euro zone, the UK and Turkey. Aengus Collins graduated from Trinity College Dublin and subsequently obtained postgraduate degrees in European Economic and Public Affairs (MEconSc) and Philosophy (MA).
