World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
50
Citations
9755
World Ranking
1084
National Ranking
38

Engineering and Technology

D-Index
53
Citations
10759
World Ranking
3393
National Ranking
141

Overview

Eldad Haber is affiliated with the University of British Columbia in Canada. Their research primarily focuses on the field of Computer Science, with prominent work in Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology, Geophysics, and Statistical and Nonlinear Physics.

Their main topics of research include:

  • Model Reduction and Neural Networks
  • Sparse and Compressive Sensing Techniques
  • Neural Networks and Applications
  • Advanced Graph Neural Networks
  • Geochemistry and Geologic Mapping
  • Geophysical and Geoelectrical Methods
  • Numerical methods in inverse problems

Eldad Haber has published extensively, with a significant number of papers appearing in various venues. Frequent publication venues include:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Inverse Problems
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Physica Scripta

Some recent papers authored or co-authored by Eldad Haber are:

  • PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equations, 2021, arXiv (Cornell University)
  • Mineral prospectivity mapping using a VNet convolutional neural network, 2021, The Leading Edge
  • LeanConvNets: Low-Cost Yet Effective Convolutional Neural Networks, 2020, IEEE Journal of Selected Topics in Signal Processing
  • Fully hyperbolic convolutional neural networks, 2022, Research in the Mathematical Sciences

Throughout their career, Eldad Haber has collaborated frequently with several researchers. Key co-authors include:

  • Moshe Eliasof
  • Eran Treister
  • Bas Peters
  • Tue Boesen
  • Keegan Lensink

Best Publications

  • Stable Architectures for Deep Neural Networks

    Eldad Haber;Lars Ruthotto

  • Intensity gradient based registration and fusion of multi-modal images.

    Eldad Haber;Jan Modersitzki

  • Deep Neural Networks Motivated by Partial Differential Equations

    Lars Ruthotto;Eldad Haber

  • On optimization techniques for solving nonlinear inverse problems

    Eldad Haber;Uri M Ascher;Doug Oldenburg

  • Joint inversion: a structural approach

    E Haber;D Oldenburg

  • Three dimensional inversion of multisource time domain electromagnetic data

    Douglas W. Oldenburg;Eldad Haber;Roman Shekhtman

  • Fast Simulation of 3D Electromagnetic Problems Using Potentials

    E. Haber;U.M. Ascher;D.A. Aruliah;D.W. Oldenburg

  • RESINVM3D: A 3D resistivity inversion package

    Adam Pidlisecky;Eldad Haber;Rosemary Knight

  • Reversible Architectures for Arbitrarily Deep Residual Neural Networks

    Bo Chang;Lili Meng;Eldad Haber;Lars Ruthotto

  • Inversion of 3D electromagnetic data in frequency and time domain using an inexact all-at-once approach

    Eldad Haber;Uri M. Ascher;Douglas W. Oldenburg

  • Fast Finite Volume Simulation of 3D Electromagnetic Problems with Highly Discontinuous Coefficients

    E. Haber;U. M. Ascher

  • Numerical methods for volume preserving image registration

    Eldad Haber;Jan Modersitzki

  • Preconditioned all-at-once methods for large, sparse parameter estimation problems

    E Haber;U M Ascher

  • An introduction to deep generative modeling

    Lars Ruthotto;Eldad Haber

  • An Effective Method for Parameter Estimation with PDE Constraints with Multiple Right-Hand Sides

    Eldad Haber;Matthias Chung;Felix Herrmann

  • A GCV based method for nonlinear ill-posed problems

    Eldad Haber;Douglas Oldenburg

  • 3-D inversion of airborne electromagnetic data parallelized and accelerated by local mesh and adaptive soundings

    Dikun Yang;Douglas W. Oldenburg;Eldad Haber

  • Computational Methods in Geophysical Electromagnetics

    Eldad Haber

  • A Multilevel Method for Image Registration

    Eldad Haber;Jan Modersitzki

  • Numerical methods for experimental design of large-scale linear ill-posed inverse problems

    E Haber;L Horesh;L Tenorio

  • Multi-level Residual Networks from Dynamical Systems View

    Bo Chang;Lili Meng;Eldad Haber;Frederick Tung

Frequent Co-Authors

Douglas W. Oldenburg
Douglas W. Oldenburg University of British Columbia
Uri M. Ascher
Uri M. Ascher University of British Columbia
Allen Tannenbaum
Allen Tannenbaum Stony Brook University
Rosemary Knight
Rosemary Knight Stanford University
Michele Benzi
Michele Benzi Scuola Normale Superiore di Pisa
Michael G. Bostock
Michael G. Bostock University of British Columbia
Tryphon T. Georgiou
Tryphon T. Georgiou University of California, Irvine
Alessandro Veneziani
Alessandro Veneziani Emory University
Hui Huang
Hui Huang Shenzhen University
Steven Constable
Steven Constable University of California, San Diego

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