Sayan BiswasExpertise
Decentralized Learning; Federated Learning; Ethical ML focusing on Privacy, Fairness, and Personalization; Location Privacy.
I am a Postdoctoral Researcher at the SaCS lab at EPFL. I completed my PhD in Computer Science from INRIA and école Polytechnique in France, under the supervision of Catuscia Palamidessi. Previously, I have been a Visiting Scholar at the School of Computing at Macquarie University in Sydney, Australia, collaborating with Annabelle McIiver and Natasha Fernandes, and at WMG, t
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
Thomas Emile BourgeatEnseignement et PhD Doctorant·es actuel·les Neelu Shivprakash Kalani, Jiawei Lin, Alexandre Pinazza, Jiacheng Ma, Martina Camaioni, Guokai Chen, Mohammadarman Soleimani Cours Computer security and privacy COM-301 C'est un cours d'introduction à la sécurité informatique et privée. L'objectif est de fournir aux étudiants les moyens de raisonner sur les problèmes de sécurité et de confidentialité et de leur donner les moyens de les affronter. Constructive Computer Architecture CS-62
Yanina Yurina ShkelEnseignement et PhD Doctorant·es actuel·les Serhat Emre Coban, Anuj Kumar Yadav, Cemre Çadir Cours Advanced probability and applications COM-417 Dans ce cours, différents aspects de la théorie des probabilités sont abordés. La première partie est dévolue à ses principaux théorèmes (loi des grands nombres, théorème central limite), tandis que la seconde partie porte sur la théorie des martingales et inégalités de concentration. Signal processing COM-202 Théorie et appli.du t
Simon Elias BibriExpertise
• Sustainable cities• Smarter eco-cities• Sustainable smart cities• Artificial Intelligence of Things (AIoT) • Generative Artificial Intelligence of Things (GAIoT)• Foundation Models (FMs) • Quantum Generative Artificial Intelligence (QGAI)• Urban Digital Twins (UDTs)• Urban Brain (UB) • Smart Circular Metabolism• Urban planning and design• Environmental sustainability and climate resilience• Sustainable smart energy• Sustainable smart transportation• Sustainable smart waste and water management•
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.
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
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,
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