Joan Bruna

About

Joan Bruna is a professor who advises students through the Computer Science department at the Courant Institute (CILVR Group), the Data Science department at the Center for Data Science (MaD group), and the Mathematics department at the Courant Institute. He provides guidance to prospective PhD students and encourages applications to the respective programs that best fit individual profiles. While he cannot address all requests from prospective MSc or undergraduate students seeking internships, he invites those with compelling stories and concrete links to his research to reach out. Currently, he is not taking any summer internships and advises prospective students to consult the program websites for further information.

Research topics

• Artificial Intelligence
• Computer Science
• Mathematics
• Machine Learning
• Theoretical computer science
• Statistical physics
• Statistics
• Combinatorics
• Physics
• Algorithm

Selected publications

• Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

  arXiv (Cornell University) · 2021 · 555 citations

  The last decade has witnessed an experimental revolution in data science and machine learning, epitomised by deep learning methods. Indeed, many high-dimensional learning tasks previously thought to be beyond reach -- such as computer vision, playing Go, or protein folding -- are in fact feasible with appropriate computational scale. Remarkably, the essence of deep learning is built from two simple algorithmic principles: first, the notion of representation or feature learning, whereby adapted,…

  DOI

• Stability Properties of Graph Neural Networks

  IEEE Transactions on Signal Processing · 2020 · 206 citations

  Graph neural networks (GNNs) have emerged as a powerful tool for nonlinear processing of graph signals, exhibiting success in recommender systems, power outage prediction, and motion planning, among others. GNNs consist of a cascade of layers, each of which applies a graph convolution, followed by a pointwise nonlinearity. In this work, we study the impact that changes in the underlying topology have on the output of the GNN. First, we show that GNNs are permutation equivariant, which implies th…

  DOI

• A new approach to observational cosmology using the scattering transform

  Monthly Notices of the Royal Astronomical Society · 2020 · 130 citations

  Senior authorCorresponding

  ABSTRACT Parameter estimation with non-Gaussian stochastic fields is a common challenge in astrophysics and cosmology. In this paper, we advocate performing this task using the scattering transform, a statistical tool sharing ideas with convolutional neural networks (CNNs) but requiring neither training nor tuning. It generates a compact set of coefficients, which can be used as robust summary statistics for non-Gaussian information. It is especially suited for fields presenting localized struct…

  PublisherOA PDFDOI

• Neural Galerkin schemes with active learning for high-dimensional evolution equations

  Journal of Computational Physics · 2023-10-24 · 35 citations

  article1st author
  PublisherDOI

• Posterior Sampling with Denoising Oracles via Tilted Transport

  arXiv (Cornell University) · 2024-06-30 · 3 citations

  preprintOpen access1st authorCorresponding

  Score-based diffusion models have significantly advanced high-dimensional data generation across various domains, by learning a denoising oracle (or score) from datasets. From a Bayesian perspective, they offer a realistic modeling of data priors and facilitate solving inverse problems through posterior sampling. Although many heuristic methods have been developed recently for this purpose, they lack the quantitative guarantees needed in many scientific applications. In this work, we introduce t…

  PublisherOA PDFDOI

Frequent coauthors

• Yann LeCun

  New York University

  37 shared

• Stéphane Mallat

  34 shared

• Anastasiia Gorbunova

  Institut des Géosciences de l'Environnement

  33 shared

• Julien Le Sommer

  Université Grenoble Alpes

  27 shared

• Samy Jelassi

  25 shared

• Julie Deshayes

  Sorbonne Université

  25 shared

• Denis Zorin

  New York University

  19 shared

• Rob Fergus

  16 shared

Labs

• Joan BrunaPI

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