CSD Seminar webpage

Welcome to the CSD seminar webpage ! 

 

This seminar aims at inviting researchers coming from all domains related to Data Science, ranging from physics and mathematics to linguistics and cognitive science. 

The talks will usually be on Thursdays from 2pm to 3pm in the conference room of the center, but since there are many workshops and seminars, they will sometimes be moved to other days of the week. 

The talks will last about 45 minutes, and will be followed by questions and discussions . 

The CSD seminar team

List of seminars : 

  • 2026/04/02, Tony Silveti-Falls, Séminaire
  • 2026/03/26, François Malgouyres, Séminaire
  • 2026/03/19, Eugène Ndiaye, Séminaire
  • 2026/03/17, Julien Mairal, Colloquium
  • 2026/03/12, Leonardo Defilippis ; Luigi Fogliani, GT
  • 2026/02/19, Thibaut Germain, Séminaire
  • 2026/02/12, Alessandro Laio, Colloquium
  • 2026/02/05, Nina Vesseron ; Johannes Hertrich, GT
  • 2026/01/29, Ashok Vardhan, From Markov to Laplace : A Markovian Tale of Large Language Models
  • 2026/01/22, Eddie Aamari, GT
  • 2026/01/15, Marine Le Morvan, A Tabular Foundation Model for In-Context Learning on Large Data
  • 2026/01/08, Nathan Srebro, Colloquium, Learning to Answer from Correct Demonstrations
  • 2025/12/18, Pierre Marion, Séminaire
  • 2025/12/11, Etienne Lempereur, Raphaël Urfin, GT
  • 2025/11/27, Jérôme Bolte, Séminaire
  • 2025/11/20, Stephen Zhang, Flow Matching for (Generalized) Schrödinger Bridges and Dynamical Optimal Transport
  • 2025/11/18, Étienne Ollion, Colloquium
  • 2025/11/13, Hugo Cui, Séminaire
  • 2025/11/06, Samuel Boïté ; Léa Bohbot, GT
  • 2025/10/30, Arie Wortsman ; Benjamin Dupuis, GT
  • 2025/10/21, Eva Dyer, Colloquium, Large-scale pretraining on neural data allows for transfer across individuals, tasks and species
  • 2025/10/16, Arthur Pellegrino ; Nikos Tsilivis, GT
  • 2025/10/09, Emile Pierret, Séminaire
  • 2025/10/02, Davide Carbone ; Nathanael Cuvelle-Magar, GT
  • 2025/09/25, Julie Delon, Computing Barycentres of Measures for Generic Transport Costs
  • 2025/06/26, Luiz Chamon, Learning Under Requirements
  • 2025/06/19, Kruno ; Luca del Bono, GT théorie
  • 2025/06/12, David Louapre, CSD Colloquium
  • 2025/05/22, Eric Vanden-Eijnden, CSD Colloquium
  • 2025/05/15, Grant Rotskoff, Séminaire
  • 2025/04/24, Loucas-Pillaud Vivien, Séminaire
  • 2025/04/17, Beatrice Achilli, GT
  • 2025/04/03, Olivier Cappé, Gabriel Peyré, GT Modern Transformers and GRPO
  • 2025/03/27, Denny Wu, Séminaire
  • 2025/03/20, Demba Ba, Ten Years of Playing in Deep Haystacks : A Taxonomy of Interpretability Research
  • 2025/03/13, Alex Cayco Gajic, Séminaire
  • 2025/03/06, Yann Fleureau, Projet Numina : IA appliquée au raisonnement et à la formalisation mathématiques
  • 2025/02/27, Antonio Ocello, Séminaire
  • 2025/02/20, Clément Bonet, Séminaire
  • 2025/02/13, Fanny Yang, CSD Colloquium
  • 2025/02/06, Maxence Noble
  • 2025/01/30, Alessandro de Palma, Séminaire
  • 2025/01/23, Michele Ceriotti, CSD Colloquium
  • 2025/01/16, Antonin Brossollet ; Etienne Lempereur, GT
  • 2025/01/09, Gauthier Thurin ; Leonardo Defilippis, GT
  • 2024/12/19, Olivier Cappé, GT
  • 2024/12/12, Emanuele Francazi ; Alexis Aymé, GT
  • 2024/12/05, Jean-Remi King, CSD Colloquium
  • 2024/11/28, Carl Allen, GT
  • 2024/11/21, Elisabetta Cornacchia, Séminaire
  • 2024/11/14, Luca Biferale, CSD Colloquium
  • 2024/11/07, Nicolas Schreuder (LIGM), Fairness in machine learning : a study of the Demographic Parity constraint
  • 2024/10/10, Gabriele Sicuro (Bologna), The random assignment problem
  • 2024/09/26, Vassilis Papadopoulos (EPFL), Arrows of Time for Large Language Models
  • 2024/09/17, Matthieu Wyart (EPFL), Learning hierarchical representations with deep nets and large language models
  • 2024/07/11, Carl Allen (ENS), Variational Classification
  • 2024/06/20, Martin de Hoop (RICE), Lecture on neural operators
  • 2024/06/13, Raphael Berthier (Sorbonne), The two timescale regime for the analysis of neural networks
  • 2024/06/06, Ehsan Elhamifar (Northeastern), Understanding Complex Procedural Videos
  • 2024/05/16, Kimia Nadjahi (ENS), Slicing Mutual Information Generalization Bounds for Neural Networks
  • 2024/03/28, Ulugbek Kamilov, Computational Imaging : Restoration Deep Networks as Image Priors
  • 2024/03/14, Florent Krzakala, How do neural nets learn ?
  • 2024/02/08, Cyril Letrouit (Orsay), Two mathematical perspectives on Transformers
  • 2024/01/25, Francis Bach (INRIA), An alternative view of denoising diffusion models
  • 2024/01/18, Lenka Zdeborová (EPFL), Backtracking Dynamical Cavity method
  • 2023/12/14, Mathieu Desbrun, Blue Noise Sampling
  • 2023/11/30, Pierre-Alexandre Mattei, Are ensembles getting better All the time ?
  • 2023/11/14, Antoine Maillard, Fitting ellipsoids to random points
  • 2023/10/17, Andrea Montanari, Sampling via diffusion processes : rigorous guarantees, hardness, disorder chaos
  • 2023/06/22, Soledad Villar, Machine learning and invariant theory
  • 2023/06/08, Antonio Silveti-Falls, Differentiating Nonsmooth Solutions to Parametric Monotone Inclusion Problems
  • 2023/05/11, Francois Lanusse, Deep Generative Models for Hybrid Physical/Data-Driven Bayesian Inference
  • 2023/04/27, Pierre Ablin, Training neural networks with orthogonal weights
  • 2023/04/13, Arthur Mensch, Flamingo : a Visual Language Model for Few-Shot Learning
  • 2023/03/16, Stéphanie Allassonnière, Data-Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder
  • 2023/03/02, James Thornton, Diffusion / Field based Reconstruction and Generation for 3D Shapes
  • 2023/02/16, Gaël Varoquaux, Embeddings to learn on messy relational data
  • 2023/01/19, Hervé Jégou, Learning image representations with coarse, instance-level and image-level supervision
  • 2022/10/20 Bruno Loureiro, Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks
  • 2022/10/13, Raja Giryes, Sampling based analysis of neural network generalization and extrapolation
  • 2022/6/16, Francis Bach, The quest for adaptivity
  • 2022/6/9, Rachel Bawden, Low-resource MT : few-shot learning and historical language normalisation
  • 2022/6/2, Thomas Moreau, Convolutional Sparse Coding for Electromagnetic Brain Signals
  • 2022/4/21, Marylou Gabrié, Enhancing Sampling with Learning
  • 2022/3/24, Charles Martin - A Semi-Empirical Model for the Generalization Capacity of Deep Neural Networks
  • 2022/2/3, Marc Lelarge - Exploiting Graph Invariants in Deep Learning
  • 2022/1/27, Sebastian Goldt - The interplay of data structure and learning dynamics in simple neural networks
  • 2021/12/16, Dieuwke Hupkes - On locality, globality, consistency and compositionality in neural machine translation
  • 2022/12/9, François Charton - Deep learning for symbolic maths
  • 2022/11/26, Mathieu Wyart - What makes data learnable by deep learning
  • 2022/11/18, Rémi Monasson - Restricted Boltzmann Machines revisited : from sampling to design