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
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
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
Sophia BeckerExpertise
Computational and theoretical neuroscienceReinforcement learning
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.
Jocelyne BlochEnseignement et PhD Doctorant·es actuel·les Camille Marie Mélodie Frayssinhes A co-dirigé les thèses EPFL de Sabry Barlatey, Aasta Parin Gandhi
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,
Mackenzie MathisCenter for NeuroprostheticsEPFL ELLIS Unit Faculty MemberCenter for Intelligent Systems
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
Brayden Graham MyersEnseignement et PhD Cours Physique générale : mécanique (anglais) PHYS-101(en) Le but du cours de physique générale est de donner à l'étudiant les notions de base nécessaires à la compréhension des phénomènes physiques. L'objectif est atteint lorsque l'étudiant est capable de prévoir quantitativement les conséquences de ces phénomènes avec des outils théoriques appropriés.
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
Paul Robert GuhennecMes recherches portent sur l'utilisation de méthodes informatiques pour l'histoire de l'architecture et leurs implications épistémologiques. Je suis actuellement chercheur post-doctoral au Digital Humanities Laboratory de l'EPFL, où j'ai récemment terminé ma thèse de doctorat sur l'histoire urbaine et architecturale de Venise, intitulée «The Skin of Venice: A Computational Approach to the City's Urban Structure».
Pasquale Davide SchiavoneEnseignement et PhD Cours Design technologies for integrated systems CS-472 La synthèse hardware est le processus de transformation - itérativement raffiné, détaillé et optimisé - d'une représentation faite en langage de description matérielle en un circuit. Ce cours présente les algorithmes, outils et méthodes de la compilation matérielle et de la synthèse logique.
Mykhailo BatiukExpertise
Single cell and spatial genomics (wet/dry lab), glia, psychiatry, neuroscience
I obtained my PhD (2012-2018) in KU Leuven/VIB, Belgium where I investigated astrocyte heterogeneity using single cell transcriptomics. During this period I developed tools to acutely and specifically isolate astrocytes from adult brain and identified five astrocyte subtypes in the cortex and hippocampus of mouse brain expanding previous understanding of astrocyte variability.Following my PhD, I did my first post-doc in
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]
