World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
40
Citations
10570
World Ranking
9096
National Ranking
147

Boaz Nadler publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Boaz Nadler sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 113 publications — 12th percentile

12% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Boaz Nadler D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Boaz Nadler sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 40 D-Index — 37th percentile

37% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Boaz Nadler is affiliated with the Weizmann Institute of Science in Israel. Their research spans multiple fields, primarily in Computer Science and Engineering. The scientist's body of work includes significant contributions to computational mechanics, artificial intelligence, molecular biology, statistics and probability, and signal processing.

The main research topics covered by Boaz Nadler involve sparse and compressive sensing techniques, machine learning and algorithms, Bayesian methods and mixture models, statistical methods and inference, blind source separation techniques, image and signal denoising methods, and genomics and phylogenetic studies.

Boaz Nadler has published extensively in various venues. Frequent publication outlets include:

  • arXiv (Cornell University)
  • SIAM Journal on Mathematics of Data Science
  • Information and Inference A Journal of the IMA
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Nature Communications

Notable recent papers are:

  • Zero-preserving imputation of single-cell RNA-seq data, 2022, Nature Communications
  • Rank 2r Iterative Least Squares: Efficient Recovery of Ill-Conditioned Low Rank Matrices from Few Entries, 2021, SIAM Journal on Mathematics of Data Science
  • GNMR: A Provable One-Line Algorithm for Low Rank Matrix Recovery, 2022, SIAM Journal on Mathematics of Data Science
  • "Self-Wiener" Filtering: Data-Driven Deconvolution of Deterministic Signals, 2021, IEEE Transactions on Signal Processing
  • Tight recovery guarantees for orthogonal matching pursuit under Gaussian noise, 2020, Information and Inference A Journal of the IMA

Collaboration has been a significant aspect of their work. Frequent co-authors include:

  • Pini Zilber
  • Yuval Kluger
  • Chen Amiraz
  • Robert Krauthgamer
  • Ariel Jaffe

Best Publications

  • Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps

    R. R. Coifman;S. Lafon;A. B. Lee;M. Maggioni

  • Diffusion maps, spectral clustering and reaction coordinates of dynamical systems

    Boaz Nadler;Stéphane Lafon;Ronald R. Coifman;Ioannis G. Kevrekidis

  • Diffusion Maps, Spectral Clustering and Eigenfunctions of Fokker-Planck Operators

    Boaz Nadler;Stephane Lafon;Ioannis Kevrekidis;Ronald R. Coifman

  • Finite sample approximation results for principal component analysis: A matrix perturbation approach

    Boaz Nadler

  • Non-Parametric Detection of the Number of Signals: Hypothesis Testing and Random Matrix Theory

    S. Kritchman;B. Nadler

  • Natural image denoising: Optimality and inherent bounds

    Anat Levin;Boaz Nadler

  • Geometric diffusions as a tool for harmonic analysis and structure definition of data: Multiscale methods

    R. R. Coifman;S. Lafon;A. B. Lee;M. Maggioni

  • Multiscale Wavelets on Trees, Graphs and High Dimensional Data: Theory and Applications to Semi Supervised Learning

    Matan Gavish;Boaz Nadler;Ronald R. Coifman

  • Determining the number of components in a factor model from limited noisy data

    Shira Kritchman;Boaz Nadler

  • SpectralNet: Spectral Clustering using Deep Neural Networks

    Uri Shaham;Kelly P. Stanton;Henry Li;Boaz Nadler

  • Diffusion Maps, Spectral Clustering and Eigenfunctions of Fokker-Planck operators

    Boaz Nadler;Stephane Lafon;Ronald R. Coifman;Ioannis G. Kevrekidis

  • Nonparametric Detection of Signals by Information Theoretic Criteria: Performance Analysis and an Improved Estimator

    Boaz Nadler

  • Accurate Blur Models vs. Image Priors in Single Image Super-resolution

    Netalee Efrat;Daniel Glasner;Alexander Apartsin;Boaz Nadler

  • Performance of Eigenvalue-Based Signal Detectors with Known and Unknown Noise Level

    Boaz Nadler;Federico Penna;Roberto Garello

  • Fundamental Limitations of Spectral Clustering

    Boaz Nadler;Meirav Galun

  • Minimax bounds for sparse PCA with noisy high-dimensional data

    Aharon Birnbaum;Iain M. Johnstone;Boaz Nadler;Debashis Paul

  • The prediction error in CLS and PLS : the importance of feature selection prior to multivariate calibration

    Boaz Nadler;Ronald R. Coifman

  • Patch complexity, finite pixel correlations and optimal denoising

    Anat Levin;Boaz Nadler;Fredo Durand;William T. Freeman

  • Ranking and combining multiple predictors without labeled data.

    Fabio Parisi;Francesco Strino;Boaz Nadler;Yuval Kluger;Yuval Kluger

  • On the optimality of averaging in distributed statistical learning

    Jonathan D. Rosenblatt;Boaz Nadler

  • Treelets--An adaptive multi-scale basis for sparse unordered data

    Ann B. Lee;Boaz Nadler;Larry Wasserman

Frequent Co-Authors

Yuval Kluger
Yuval Kluger Yale University
Ronald R. Coifman
Ronald R. Coifman Yale University
Ronen Basri
Ronen Basri Weizmann Institute of Science
Amit Singer
Amit Singer Princeton University
Ioannis G. Kevrekidis
Ioannis G. Kevrekidis Johns Hopkins University
Robert Krauthgamer
Robert Krauthgamer Weizmann Institute of Science
Irvin M. Modlin
Irvin M. Modlin Yale University
Mark Kidd
Mark Kidd Wren Laboratories
Fred A. Hamprecht
Fred A. Hamprecht Heidelberg University
Larry Wasserman
Larry Wasserman Carnegie Mellon University

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