World's Best Scientists 2026 revealed!
Lorenzo Rosasco

Lorenzo Rosasco

D-Index & Metrics

Mathematics

D-Index
51
Citations
10577
World Ranking
1025
National Ranking
476

Computer Science

D-Index
51
Citations
11307
World Ranking
5308
National Ranking
2442

Lorenzo Rosasco publication distribution in Mathematics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mathematics in 2026. The highlighted bar marks where Lorenzo Rosasco sits on this spectrum.

42–46 publications: 3 scientists 47–51 publications: 5 scientists 52–56 publications: 7 scientists 57–61 publications: 20 scientists 62–66 publications: 14 scientists 67–71 publications: 25 scientists 72–76 publications: 19 scientists 77–81 publications: 35 scientists 82–86 publications: 50 scientists 87–91 publications: 60 scientists 92–96 publications: 86 scientists 97–101 publications: 84 scientists 102–106 publications: 83 scientists 107–111 publications: 90 scientists 112–116 publications: 99 scientists 117–121 publications: 90 scientists 122–126 publications: 91 scientists 127–131 publications: 109 scientists 132–136 publications: 110 scientists 137–141 publications: 98 scientists 142–146 publications: 112 scientists 147–151 publications: 102 scientists 152–156 publications: 88 scientists 157–161 publications: 106 scientists 162–166 publications: 83 scientists 167–171 publications: 102 scientists 172–176 publications: 77 scientists 177–181 publications: 81 scientists 182–186 publications: 78 scientists 187–191 publications: 71 scientists 192–196 publications: 92 scientists 197–201 publications: 64 scientists 202–206 publications: 69 scientists 207–211 publications: 64 scientists 212–216 publications: 62 scientists 217–221 publications: 58 scientists 222–226 publications: 53 scientists 227–231 publications: 50 scientists 232–236 publications: 46 scientists 237–241 publications: 46 scientists 242–246 publications: 46 scientists 247–251 publications: 43 scientists 252–256 publications: 29 scientists 257–261 publications: 45 scientists 262–266 publications: 30 scientists 267–271 publications: 33 scientists 272–276 publications: 34 scientists 277–281 publications: 30 scientists 282–286 publications: 31 scientists 287–291 publications: 21 scientists 292–296 publications: 34 scientists 297–301 publications: 26 scientists 302–306 publications: 10 scientists 307–311 publications: 17 scientists 312–316 publications: 23 scientists 317–321 publications: 13 scientists 322–326 publications: 16 scientists 327–331 publications: 26 scientists 332–336 publications: 13 scientists 337–341 publications: 13 scientists 342–346 publications: 16 scientists 347–351 publications: 17 scientists 352–356 publications: 12 scientists 357–361 publications: 18 scientists 362–366 publications: 18 scientists 367–371 publications: 9 scientists 372–376 publications: 11 scientists 377–381 publications: 8 scientists 382–386 publications: 8 scientists 387–391 publications: 9 scientists 392–396 publications: 9 scientists 397–401 publications: 8 scientists 402–406 publications: 11 scientists 407–411 publications: 6 scientists 412–416 publications: 6 scientists 417–421 publications: 9 scientists 422–426 publications: 8 scientists 427–431 publications: 5 scientists 432–436 publications: 8 scientists 437–441 publications: 8 scientists 442–446 publications: 4 scientists 447–451 publications: 4 scientists 452–456 publications: 4 scientists 457–461 publications: 2 scientists 462–466 publications: 2 scientists 467–471 publications: 4 scientists 472–476 publications: 3 scientists 477–481 publications: 3 scientists 482–486 publications: 6 scientists 487–491 publications: 3 scientists 492–496 publications: 5 scientists 497–501 publications: 5 scientists 502–506 publications: 1 scientists 507–511 publications: 6 scientists 512–516 publications: 4 scientists 517–521 publications: 1 scientists 522–526 publications: 3 scientists 527–531 publications: 1 scientists 532–536 publications: 4 scientists 537+ publications: 100 scientists
42 publications 537+

This scientist: 191 publications — 58th percentile

58% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 537 publications or more.

Lorenzo Rosasco D-index placement in Mathematics in 2026

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

30 D-Index: 174 scientists 31 D-Index: 151 scientists 32 D-Index: 174 scientists 33 D-Index: 117 scientists 34 D-Index: 136 scientists 35 D-Index: 127 scientists 36 D-Index: 145 scientists 37 D-Index: 153 scientists 38 D-Index: 150 scientists 39 D-Index: 150 scientists 40 D-Index: 138 scientists 41 D-Index: 136 scientists 42 D-Index: 93 scientists 43 D-Index: 108 scientists 44 D-Index: 115 scientists 45 D-Index: 112 scientists 46 D-Index: 103 scientists 47 D-Index: 75 scientists 48 D-Index: 59 scientists 49 D-Index: 67 scientists 50 D-Index: 60 scientists 51 D-Index: 57 scientists 52 D-Index: 59 scientists 53 D-Index: 62 scientists 54 D-Index: 60 scientists 55 D-Index: 50 scientists 56 D-Index: 42 scientists 57 D-Index: 54 scientists 58 D-Index: 50 scientists 59 D-Index: 42 scientists 60 D-Index: 41 scientists 61 D-Index: 35 scientists 62 D-Index: 40 scientists 63 D-Index: 21 scientists 64 D-Index: 31 scientists 65 D-Index: 27 scientists 66 D-Index: 29 scientists 67 D-Index: 19 scientists 68 D-Index: 25 scientists 69 D-Index: 17 scientists 70 D-Index: 18 scientists 71 D-Index: 12 scientists 72 D-Index: 14 scientists 73 D-Index: 13 scientists 74 D-Index: 18 scientists 75 D-Index: 9 scientists 76 D-Index: 11 scientists 77 D-Index: 10 scientists 78 D-Index: 9 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 10 scientists 82 D-Index: 5 scientists 83 D-Index: 5 scientists 84 D-Index: 13 scientists 85 D-Index: 6 scientists 86+ D-Index: 99 scientists
30 D-Index 86+

This scientist: 51 D-Index — 73rd percentile

73% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 86 D-Index or more.

Overview

Lorenzo Rosasco is affiliated with MIT in the United States and has an extensive research portfolio spanning several areas within computer science and engineering. The primary fields of study in Rosasco's work are Computer Science and Engineering, reflecting a broad interdisciplinary approach to research challenges.

The scientist's subfields of study include:

  • Artificial Intelligence
  • Computational Mechanics
  • Computer Vision and Pattern Recognition
  • Biomedical Engineering
  • Mathematical Physics

Rosasco's main research topics encompass:

  • Sparse and Compressive Sensing Techniques
  • Numerical methods in inverse problems
  • Domain Adaptation and Few-Shot Learning
  • Gaussian Processes and Bayesian Inference
  • Stochastic Gradient Optimization Techniques
  • Statistical Methods and Inference
  • Machine Learning and Data Classification

The scientist has collaborated frequently with several researchers in the field. Notable frequent co-authors include:

  • Ernesto De Vito
  • Silvia Villa
  • Marco Rando
  • Cesare Molinari
  • Lorenzo Natale

Regarding publication venues, Rosasco has contributed extensively to:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • eLife
  • CINECA IRIS Institutional Research Information System (University of Genoa)
  • Analysis and Applications

Recent papers authored or co-authored by Rosasco include:

  • Learning to predict target location with turbulent odor plumes, 2022, eLife
  • Learning new physics efficiently with nonparametric methods, 2022, The European Physical Journal C
  • In-domain versus out-of-domain transfer learning in plankton image classification, 2023, Scientific Reports
  • Kernel methods through the roof: handling billions of points efficiently, 2020, arXiv (Cornell University)
  • Understanding neural networks with reproducing kernel Banach spaces, 2022, Applied and Computational Harmonic Analysis

Best Publications

  • Holographic embeddings of knowledge graphs

    Maximilian Nickel;Lorenzo Rosasco;Tomaso Poggio

  • On Early Stopping in Gradient Descent Learning

    Yuan Yao;Lorenzo Rosasco;Lorenzo Rosasco;Andrea Caponnetto;Andrea Caponnetto

  • Kernels for Vector-Valued Functions: A Review

    Mauricio A. Álvarez;Lorenzo Rosasco;Neil D. Lawrence

  • Why and When Can Deep – but Not Shallow – Networks Avoid the Curse of Dimensionality: a Review

    Tomaso A. Poggio;Hrushikesh Mhaskar;Hrushikesh Mhaskar;Lorenzo Rosasco;Brando Miranda

  • Are loss functions all the same

    Lorenzo Rosasco;Ernesto De Vito;Andrea Caponnetto;Michele Piana

  • Elastic-net regularization in learning theory

    Christine De Mol;Ernesto De Vito;Lorenzo Rosasco

  • On regularization algorithms in learning theory

    Frank Bauer;Sergei Pereverzev;Lorenzo Rosasco

  • Learning from Examples as an Inverse Problem

    Ernesto De Vito;Lorenzo Rosasco;Andrea Caponnetto;Umberto De Giovannini

  • Generalization Properties of Learning with Random Features

    Alessandro Rudi;Lorenzo Rosasco

  • Less is more: Nyström computational regularization

    Alessandro Rudi;Raffaello Camoriano;Lorenzo Rosasco

  • On Learning with Integral Operators

    Lorenzo Rosasco;Mikhail Belkin;Ernesto De Vito

  • Model Selection for Regularized Least-Squares Algorithm in Learning Theory

    E. De Vito;A. Caponnetto;L. Rosasco

  • Spectral algorithms for supervised learning

    L. Lo Gerfo;L. Rosasco;F. Odone;E. De Vito

  • Convergence of Stochastic Proximal Gradient Algorithm

    Lorenzo Rosasco;Lorenzo Rosasco;Silvia Villa;Bằng Công Vũ

  • Iterative Projection Methods for Structured Sparsity Regularization

    Lorenzo Rosasco;Alessandro Verri;Matteo Santoro;Sofia Mosci

  • Some Properties of Regularized Kernel Methods

    Ernesto De Vito;Lorenzo Rosasco;Andrea Caponnetto;Michele Piana

  • Solving structured sparsity regularization with proximal methods

    Sofia Mosci;Lorenzo Rosasco;Matteo Santoro;Alessandro Verri

  • FALKON: An Optimal Large Scale Kernel Method

    Alessandro Rudi;Luigi Carratino;Lorenzo Rosasco

  • Unsupervised learning of invariant representations

    Fabio Anselmi;Joel Z. Leibo;Lorenzo Rosasco;Jim Mutch

  • Unsupervised Learning of Invariant Representations in Hierarchical Architectures

    Fabio Anselmi;Joel Z. Leibo;Lorenzo Rosasco;Jim Mutch

  • Theory of Deep Learning III: explaining the non-overfitting puzzle

    Tomaso A. Poggio;Kenji Kawaguchi;Qianli Liao;Brando Miranda

  • Less is More: Nystr"om Computational Regularization

    Alessandro Rudi;Raffaello Camoriano;Lorenzo Rosasco

Frequent Co-Authors

Alessandro Verri
Alessandro Verri University of Genoa
Lorenzo Natale
Lorenzo Natale Italian Institute of Technology
Giorgio Metta
Giorgio Metta Italian Institute of Technology
Hrushikesh N. Mhaskar
Hrushikesh N. Mhaskar Claremont Graduate University
Joel Z. Leibo
Joel Z. Leibo DeepMind (United Kingdom)
Luigi Varesio
Luigi Varesio MRC Laboratory of Molecular Biology
Steve Smale
Steve Smale City University of Hong Kong
Ding-Xuan Zhou
Ding-Xuan Zhou University of Sydney
Alessandro Lazaric
Alessandro Lazaric Facebook (United States)

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