Haggai Maron | Research

Haggai Maron

Haggai Maron joined Nvidia Research in October 2019 as a Research Scientist. His main fields of interest are machine learning and shape analysis. In particular, he studies how to apply deep learning to irregular domains (e.g., sets, graphs, point clouds, and surfaces) by leveraging their symmetry structure.

Haggai completed his MSc and Ph.D. at the Department of Computer Science and Applied Mathematics at the Weizmann Institute of Science, and his BSc in Mathematics and Computer Science from the Hebrew University of Jerusalem.

Personal website: https://haggaim.github.io/

Research Area(s)

Computer Graphics

Computer Vision

Main Field of Interest

Artificial Intelligence and Machine Learning

Google Scholar

https://scholar.google.co.il/citations?user=4v8uJrIAAAAJ

Publications

2025

Directed Graph Generation with Heat Kernels

Marc T. Law, Karsten Kreis, Haggai Maron

Transactions on Machine Learning Research (TMLR) 2025

2024

Fast Encoder-Based 3D from Casual Videos via Point Track Processing

Yoni Kasten, Wuyue Lu, Haggai Maron

2023

Graph Metanetworks for Processing Diverse Neural Architectures

Derek Lim, Haggai Maron, Marc T. Law, Jonathan Lorraine, James Lucas

Norm-guided latent space exploration for text-to-image generation

Dvir Samuel, Rami Ben-Ari, Nir Darshan, Haggai Maron, Gal Chechik

Equivariant Architectures for Learning in Deep Weight Spaces

Aviv Navon, Aviv Shamsian, Idan Achituve, Ethan Fetaya, Gal Chechik, Haggai Maron

2022

Optimizing tensor network contraction using reinforcement learning

Eli Meirom, Haggai Maron, Shie Mannor, Gal Chechik

International Conference on Machine Learning, PMLR 162:15278-15292, 2022

StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators

Rinon Gal, Or Patashnik, Haggai Maron, Amir Bermano, Gal Chechik, Daniel Cohen-Or

2021

From local structures to size generalization in graph neural networks

Gilad Yehudai, Ethan Fetaya, Eli Meirom, Gal Chechik, Haggai Maron

Controlling graph dynamics with reinforcement learning and graph neural networks

Eli Meirom, Haggai Maron, Shie Mannor, Gal Chechik

Auxiliary Learning by Implicit Differentiation

Aviv Navon, Idan Achituve, Haggai Maron, Gal Chechik, Ethan Fetaya

On the Universality of Rotation Equivariant Point Cloud Networks

Nadav Dym, Haggai Maron

Federated Learning with Heterogeneous Architectures using Graph HyperNetworks

Or Litany, Haggai Maron, David Acuna, Jan Kautz, Gal Chechik, Sanja Fidler

Self-Supervised Learning for Domain Adaptation on Point-Clouds

Idan Achituve, Haggai Maron, Gal Chechik

Winter Conference on Applications of Computer Vision (WACV), 2021

2020

Set2Graph: Learning Graphs From Sets

Hadar Serviansky, Nimrod Segol, Jonathan Shlomi, Kyle Cranmer, Eilam Gross, Haggai Maron, Yaron Lipman

Conference on Neural Information Processing Systems (NeurIPS 2020)

Learning Algebraic Multigrid Using Graph Neural Networks

Ilay Luz, Meirav Galun, Haggai Maron, Ronen Basri, Irad Yavneh

International Conference on Machine Learning (ICML) 2020

On Learning Sets of Symmetric Elements

Haggai Maron, Or Litany, Gal Chechik, Ethan Fetaya

International Conference on Machine Learning (ICML) 2020

Outstanding Paper, ICML 2020

Graph Positional Encoding via Random Feature Propagation

Moshe Eliasof, Fabrizio Frasca, Beatrice Bevilacqua, Eran Treister, Gal Chechik, Haggai Maron