People

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
Generative AI
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
NeurIPS 2024

2023

Graph Metanetworks for Processing Diverse Neural Architectures
Derek Lim, Haggai Maron, Marc T. Law, Jonathan Lorraine, James Lucas
ICLR
Norm-guided latent space exploration for text-to-image generation
Dvir Samuel, Rami Ben-Ari, Nir Darshan, Haggai Maron, Gal Chechik
NeurIPS 2023
Equivariant Architectures for Learning in Deep Weight Spaces
Aviv Navon, Aviv Shamsian, Idan Achituve, Ethan Fetaya, Gal Chechik, Haggai Maron
ICML 2023

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
SIGGRAPH 2022

2021

Controlling graph dynamics with reinforcement learning and graph neural networks
Eli Meirom, Haggai Maron, Shie Mannor, Gal Chechik
ICML 2021
From local structures to size generalization in graph neural networks
Gilad Yehudai, Ethan Fetaya, Eli Meirom, Gal Chechik, Haggai Maron
ICML 2021
On the Universality of Rotation Equivariant Point Cloud Networks
Nadav Dym, Haggai Maron
ICLR 2021
Auxiliary Learning by Implicit Differentiation
Aviv Navon, Idan Achituve, Haggai Maron, Gal Chechik, Ethan Fetaya
ICLR 2021
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
ICML 2023