• Multimodal PreTraining for Scientific Data - Berkeley, Oct 24

    Presentation at the BLASS 2024 meeting in Berkeley, CA

  • Multimodal PreTraining for Scientific Data - Paris, Oct 24

  • Multimodal PreTraining for Scientific Data - Oct 24

  • Towards Multidisciplinary Scientific Foundation Models

    Overview talk of the Polymathic AI Initiative

  • Intro to Self Supervised Representation Learning for Astrophysics

  • Multimodal PreTraining for Scientific Data

  • Towards Self Supervised Representation Learning

  • AI4Phys

  • Towards Foundation Models for Science

    Overview talk of the Polymathic AI Initiative

  • AstroCLIP

  • NuitAstro

    Comment les machines apprennent-elles l'Univers ?

  • Introduction to Probabilistic Learning in JAX

    Probabilistic Learning lecture at Kavli IPMU, April 2023

  • DAp GPU Day

  • jax-hpc

    A little overview of how to use JAX for High Performance Computing on GPU clusters

  • Deep Probabilistic Learning

    Probabilistic Learning lecture at Advanced Euclid School, June 2022

  • Deep Probabilistic Learning

    Probabilistic Learning lecture

  • L'intelligence Artificielle a la Decouverte du Cosmos

    Astro cafe a Seolane, 8 Decembre 2021

  • Deep Probabilistic Learning

    Probabilistic Learning lecture at Astroinfo 2021

  • jax-cosmo

    Introduction to JAX and the jax-cosmo library

  • Commentary on Stella Offner's talk

  • IDRIS Hackathon Intro

  • Deep Probabilistic Learning

    MML - Cours 11, April 9th 2021

  • Introduction to Generative Modeling

    Slides for ANF Machine Learning

  • The DESC Tomo Challenge

    An introduction to the challenge

  • A quick tour of Neural Networks for Time Series

  • Introducing the Differentiable Universe Initiative

  • Deep Probabilistic Learning

    ML Club session of Thursday November 7th 2019

  • Anomaly Detection meetsDeep Learning

    Session of the BIDS ML & Stats forum on anomaly detection

  • Bayesian Neural Networks

    Session on modeling uncertainties with neural networks, for the Berkeley Statistics and Machine Learning Forum

  • Introduction to Machine Learning

    Bayesian Data Analysis And Machine Learning for Physical Sciences

  • Recurrent Inferrence Machines

    Session on RIMs for the Berkeley Statistics and Machine Learning Forum

  • Introduction to Deep Probabilistic Learning

  • Data-driven galaxy morphology models

    Presentation for Blending Task Force meeting, Jan. 7 2019

  • Optimization

    Practical statistics series

  • Looking for dark matter at the bottom of a wine glass

    Astronomy on Tap presentation, April 26 2018