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
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
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
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
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.
Mackenzie MathisCenter for NeuroprostheticsEPFL ELLIS Unit Faculty MemberCenter for Intelligent Systems
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
Jonathan Yuelin DongEnseignement et PhD Doctorant·es actuel·les Zhiyuan Hu A co-dirigé les thèses EPFL de Yan Liu
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.
Christoph BostedtEnseignement et PhD Doctorant·es actuel·les Eloisa Florence Manetti, Wenyi Jiang, Hankai Zhang, Loïc Lucien Cédric Bassement, Silas Strebel, Eliot Jermann A dirigé les thèses EPFL de Jonas Knurr, Ningchen Yang Cours Optical methods in chemistry CH-417 Introduction et application d'outils à base de photons pour la chimie : des concepts de base aux lasers optiques et lasers à rayons X Physical Chemistry Seminar 1: Trends CH-643 Physical Chemistry Seminar 2: Highlights
Valerio ZerbiDr. Valerio Zerbi is a pioneer of preclinical magnetic resonance functional neuroimaging. At his lab at EPFL, Valerio and his team use functional MRI recordings in rodents in combination with neural modulation and computational approaches to study the mechanisms that link cellular activity to large-scale network (dys)function. His long-term ambition is to advance the development of personalised strategies based on imaging and artificial intelligence, for the diagnosis and prognosis of developmental brain disorders. Valerio is the recipient of the ETH Postdoctoral (2014), the Swiss National Science Foundation AMBIZIONE
(2017) and ECCELLENZA (2021) fellowships.
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
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
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
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
