Stephan MorgenthalerEDUCATION
Ph.D., Statistics, Princeton University, Princeton, 1983
Diplôme, Mathématiques, Ecole polytechnique fédérale de Zurich, 1979
CARRIÈRE ACADEMIQUE
Professeur de statistique appliquée, EPFL, 1991-présent
Professeur extraordinaire, statistique appliquée, EPFL, 1988-1991
Professeur associé, statistique, Yale University, 1987-1988
Professeur assistant, statistique, Yale University, 1984-1987
Instructor, mathématiques, Massachusetts Institute of Technology, 1983-1984
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
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,
Pascal FuaPascal Fua received an engineering degree from Ecole Polytechnique, Paris, in 1984 and the Ph.D. degree in Computer Science from the University of Orsay in 1989. He then worked at SRI International and INRIA Sophia-Antipolis as a Computer Scientist. He joined EPFL in 1996 where he is now a Professor in the School of Computer and Communication Science and heads the Computer Vision Laboratory. His research interests include shape modeling and motion recovery from images, analysis of microscopy images, and Augmented Reality. His research interests include shape modeling and motion recovery from images, analysis of microscopy images, and machine learning. He has (co)authored over 300 publications in refereed journals and conferences. He is an IEEE Fellow and has been an Associate Editor of IEEE journal Transactions for Pattern Analysis and Machine Intelligence. He often serves as program committee member, area chair, and program chair of major vision conferences and has cofounded three spinoff companies (Pix4D, PlayfulVision, and NeuralConcept).
Lesya ShchutskaEnseignement et PhD Doctorant·es actuel·les Elena De Santis, Yiyang Ding, Pierre Mayencourt, Spencer Collaviti, Raphaël van Laak, Zhibin Yang, Vasilisa Guliaeva, Anni Kauniskangas, Valerii Kholoimov A dirigé les thèses EPFL de Sara Celani, Serhii Cholak, Joanna Malgorzata Wanczyk, Mascellani Anna, Sonia Amina Bouchiba, Paul De Bryas Cours Particle physics II PHYS-416 Ce cours vise à familiariser les étudiants avec les principaux concepts de la physique des particules, en établissant un lie
Guido HaefeliEnseignement et PhD Doctorant·es actuel·les Federico Ronchetti, Gauri Napoletano A co-dirigé les thèses EPFL de Axel Kuonen, Olivier Göran Girard, Carina Trippl Cours Particle detection PHYS-440 Introduction des différentes interactions des particules dans la matière. Différentes techniques de détection comme scintillateurs, détecteurs à gas, silicium, et détecteurs pour identification de particules.
Michaela HirschmannEnseignement et PhD Doctorant·es actuel·les Patrick Hirling, Jonathan Petersson, Lucie Scharré Cours Astrophysics III : galaxy formation and evolution PHYS-465 Ce cours décrit les propriétés observationnelles des galaxies ainsi que les processus physiques à l'origine de leur évolutiontels qu'ils sont perçus par les modèles théoriques et numériques. Physique générale : thermodynamique (anglais) PHYS-106(en) Donner à l'étudiant les notions de base nécessaires à la compréhensi
Luc PatinyExpertise
Cheminformatics, Data management, Data processing, Javascript developmentLast updated on April 16, 2023.
GitHub
https://github.com/lpatiny
Enseignement et PhD Cours Analyse structurale (pour SFU) UNIL-129 Acquisition des notions fondamentales liées à l'identification de la structure de petites molécules organiques au moyen des techniques de spectrométrie de masse, spectroscopie infra-rouge et résonance magnétique nucléaire. Basic and advanced NMR - Level 1 A (EPFL) CH-60 Mathieu LemayEnseignement et PhD A co-dirigé les thèses EPFL de Martin Proença Cours Applied biomedical signal processing EE-512 L'objectif du cours est de (1) introduire des concept physiologiques de base, des notions d'acquisition de signaux et de présenter l'état-de-l'art du traitement de signaux, et (2) de proposer des examples concrêts d'applications aux monitoring de paramêtres vitaux et de diagnostic.
Roberto CastelloRoberto Castello is a senior scientist and group leader at the EPFL Laboratory of Solar Energy and Building Physics. Physicist by training, he has extensive experience in collecting, classifying and interpreting large datasets using advanced data mining techniques and statistical methods. He received his MSc (2007) in Particle Physics and PhD (2010) in Physics and Astrophysics from the University of Torino. He worked as a postdoctoral researcher at the Belgian National Research Fund (2011-2014) and at the CERN Experimental Physics Department (2015-2017) as a research fellow and data scientist. He is primary author of more than 20 peer-reviewed publications and he presented at major international conferences in the high energy physics domain.
In 2018 he joined the Solar Energy and Building Physics Laboratory (LESO-PB) to work on data mining and Machine Learning techniques for the built environment and renewable energy. His main research interests are: spatio-temporal modeling of renewable energy potential, energy consumption forecasting techniques, anomaly detection, and computer vision techniques for automated classification in the built environment.
He leads the group of Urban Data Mining, Intelligence and Simulation at LESO-PB and he is a member of the NRP75 Big Data project (HyEnergy) of the Swiss National Science Foundation. He is a member of the Swiss Competence Centre for Energy Research (SCCER) and deputy leader of the working group on Leveraging Ubiquitous Energy Data. He has served as a scientific committee member, workshop organizer and speaker at international conferences (ICAE 2020, Applied Machine Learning Days 2019 and 2020, CISBAT 2019 and 2021 and SDS2020).
Since 2017 he is member of the Geneva 2030 Ecosystem network, promoting the United Nations agenda towards the realization of the Sustainable Development Goals (SDGs).
