IC Building at EPFL

FLAIR @ EPFL


This is the homepage of the Foundations of Learning and AI Research at EPFL in Lausanne, Switzerland.

FLAIR aims at providing grounded scientific foundations to machine learning to foster the next generation of artificial intelligence models.

FLAIR connects several schools and institutes at EPFL, with graduate programs (PhD and MSc) in computer science, electrical engineering , mathematics and physics.

We have FLAIR postdoctoral fellowships available. Apply here by November 30.

The Foundations of Learning and AI Research (FLAIR) group in Lausanne proudly contributes to @neurips with about 30 papers this year from its members.

We are committed @ providing grounded scientific foundations to machine learning and foster the next generation of AI models! pic.twitter.com/0kszGIfnFD

— flair_epfl (@flair_epfl) September 30, 2023




People


Faculty

  • Emmanuel Abbe
  • Nicolas Boumal
  • Lenaic Chizat
  • Volkan Cevher
  • Nicolas Flammarion
  • Michael Gastpar
  • Florent Krzakala
  • Martin Jaggi
  • Rüdiger Urbanke
  • Matthieu Wyart
  • Lenka Zdeborová

Postdocs

  • Damien Barbier
  • Raphael Berthier
  • Francesco Cagnetta
  • Francesco Croce
  • Guillaume Dalle
  • Radu Dragomir
  • Nikita Doikov
  • Vittorio Erba
  • Christian Keup
  • Ashok Makkuva
  • Andrew McRae
  • Pierre Mergny
  • Colin Sandon
  • Antonio Sclocchi
  • Ludovic Stephan
  • Tomas Vaskevicius

PhD Students

  • Maksym Andriushchenko
  • Freya Behrens
  • Marco Bondaschi
  • El Mahdi Chayti
  • Lucas Clarte
  • Christopher Criscitiello
  • Hugo Cui
  • Yatin Dandi
  • Francesco D'Angelo
  • Odilon Duranthon
  • Dongyang Fan
  • Simin Fan
  • Alessandro Favero
  • Davide Ghio
  • Diba Hashemi
  • Cédric Koller
  • Anastasia Koloskova
  • Atli Kosson
  • Aryo Lotfi
  • Bettina Messmer
  • Amirkeivan Mohtashami
  • Andreea Musat
  • Matteo Pagliardini
  • Hristo Papazov
  • Luca Pesce
  • Scott Pesme
  • Giovanni Piccioli
  • Aditya Pradeep
  • Quentin Rebjock
  • Vinitra Swamy
  • Umberto Tomasini
  • Emanuele Troiani
  • Aditya Varre
  • Matteo Viluchio
  • Maria-Luiza Vladarean
  • Guillaume Wang
  • Gizem Yüce
  • Oğuz Kaan Yüksel

Courses


Undergraduate Courses

  • MATH-329 Continuous optimization
  • PHYS-231 Data analysis for physics

Graduate (MSc & PhD) Courses

  • COM-406 Foundations of Data Science
  • CS-433 Machine Learning
  • CS-439 Optimization For Machine Learning
  • EE-411 Fundamentals of inference and learning
  • EE-556 Mathematics of data: from theory to computation
  • EE-568 Reinforcement learning
  • EE-735 Online learning in games
  • MATH-512 Optimization on manifolds
  • MATH-520 Topics in machine learning
  • MATH-602 Inference on graphs
  • PHYS-435 Statistical Physics III
  • PHYS-467 Machine learning for physicists
  • PHYS-512 Statistical physics of computation
  • PHYS-642 Statistical physics for optimization learning
  • PHYS-754 Lecture series on scientific machine learning

Events


For further information regarding activities, sign up to our emailing lists by sending emails to FLAIR-subscribe@listes.epfl.ch
  • FLAIR Seminar
  • FLAIR Tutorial
We have also be part of the organization of the past events
  • AMLD Generative AI Workshops on Technology of Foundation Models
  • Learning: Optimization and Stochastics workshop
  • NeurIPS at EPFL

Papers


FLAIR publishes papers in top conferences like NeurIPS, ICML, ICLR, and COLT. Check out our full list of publications here.