World's Best Scientists 2026 revealed!
Aurelien Lucchi

Aurelien Lucchi

D-Index & Metrics

Computer Science

D-Index
32
Citations
15705
World Ranking
12838
National Ranking
208

Aurelien Lucchi 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 Aurelien Lucchi 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: 126 publications — 17th percentile

17% of scientists in this discipline score the same or lower.

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

Aurelien Lucchi 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 Aurelien Lucchi 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: 32 D-Index — 10th percentile

10% of scientists in this discipline score the same or lower.

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

Overview

Aurelien Lucchi is affiliated with ETH Zurich in Switzerland, focusing on research primarily within the field of Computer Science. Their work spans several subfields including Artificial Intelligence, Computational Mechanics, Computer Vision and Pattern Recognition, Management Science and Operations Research, and Statistical and Nonlinear Physics.

The researcher's main areas of study encompass a variety of topics, notably:

  • Stochastic Gradient Optimization Techniques
  • Neural Networks and Applications
  • Sparse and Compressive Sensing Techniques
  • Generative Adversarial Networks and Image Synthesis
  • Advanced Bandit Algorithms Research
  • Model Reduction and Neural Networks
  • Galaxies: Formation, Evolution, Phenomena

Aurelien Lucchi has contributed to several recent publications in distinguished venues. Key papers include:

  • "A machine learning-based surrogate model to approximate optimal building retrofit solutions," 2020, Applied Energy
  • "Variational quantum Boltzmann machines," 2021, Quantum Machine Intelligence
  • "A convolutional neural network for classifying cloud particles recorded by imaging probes," 2020, Atmospheric Measurement Techniques
  • "Full wCDM analysis of KiDS-1000 weak lensing maps using deep learning," 2022, Physical Review. D/Physical Review. D.
  • "Learning Generative Models of Textured 3D Meshes from Real-World Images," 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Frequent publication venues where Aurelien Lucchi's work appears include:

  • arXiv (Cornell University)
  • Physical Review. D/Physical Review. D.
  • Repository for Publications and Research Data (ETH Zurich)
  • Zenodo (CERN European Organization for Nuclear Research)
  • Applied Energy

Among frequent co-authors with whom Aurelien Lucchi has collaborated are:

  • Antonio Orvieto
  • Thomas Hofmann
  • Frank Proske
  • Hans Kersting
  • Dario Pavllo

Best Publications

  • SLIC Superpixels Compared to State-of-the-Art Superpixel Methods

    R. Achanta;A. Shaji;K. Smith;A. Lucchi

  • Quantum Generative Adversarial Networks for Learning and Loading Random Distributions

    Christa Zoufal;Christa Zoufal;Aurélien Lucchi;Stefan Woerner

  • Stabilizing Training of Generative Adversarial Networks through Regularization

    Kevin Roth;Aurelien Lucchi;Sebastian Nowozin;Thomas Hofmann

  • Learning Aerial Image Segmentation From Online Maps

    Pascal Kaiser;Jan Dirk Wegner;Aurelien Lucchi;Martin Jaggi

  • Supervoxel-Based Segmentation of Mitochondria in EM Image Stacks With Learned Shape Features

    A. Lucchi;K. Smith;R. Achanta;G. Knott

  • The power of quantum neural networks

    Amira Abbas;David Sutter;Christa Zoufal;Aurélien Lucchi

  • Radio frequency interference mitigation using deep convolutional neural networks

    Joël Akeret;Chihway L. Chang;Aurélien Lucchi;Alexandre Réfrégier

  • Topological Map Extraction From Overhead Images

    Zuoyue Li;Jan Dirk Wegner;Aurelien Lucchi

  • Cosmological constraints with deep learning from KiDS-450 weak lensing maps

    Janis Fluri;Tomasz Kacprzak;Aurelien Lucchi;Alexandre Refregier

  • Fast cosmic web simulations with generative adversarial networks

    Andres C. Rodríguez;Tomasz Kacprzak;Aurelien Lucchi;Adam Amara

  • Probabilistic Bag-Of-Hyperlinks Model for Entity Linking

    Octavian-Eugen Ganea;Marina Ganea;Aurelien Lucchi;Carsten Eickhoff

  • Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification

    Jan Deriu;Aurelien Lucchi;Valeria De Luca;Aliaksei Severyn

  • SwissCheese at SemEval-2016 Task 4: Sentiment Classification Using an Ensemble of Convolutional Neural Networks with Distant Supervision

    Jan Deriu;Maurice Gonzenbach;Fatih Uzdilli;Aurélien Lucchi

  • A fully automated approach to segmentation of irregularly shaped cellular structures in EM images

    Aurélien Lucchi;Kevin Smith;Radhakrishna Achanta;Vincent Lepetit

  • Stabilizing Training of Generative Adversarial Networks through Regularization

    Kevin Roth;Aurelien Lucchi;Sebastian Nowozin;Thomas Hofmann

  • Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets

    Aurelien Lucchi;Yunpeng Li;Pascal Fua

  • Variance reduced stochastic gradient descent with neighbors

    Thomas Hofmann;Aurelien Lucchi;Simon Lacoste-Julien;Brian McWilliams

  • A machine learning-based surrogate model to approximate optimal building retrofit solutions

    Emmanouil Thrampoulidis;Emmanouil Thrampoulidis;Georgios Mavromatidis;Aurelien Lucchi;Kristina Orehounig

  • Sub-sampled Cubic Regularization for Non-convex Optimization

    Jonas Moritz Kohler;Aurelien Lucchi

  • Local Saddle Point Optimization: A Curvature Exploitation Approach

    Leonard Adolphs;Hadi Daneshmand;Aurelien Lucchi;Thomas Hofmann

  • Structured Image Segmentation Using Kernelized Features

    Aurélien Lucchi;Yunpeng Li;Kevin Smith;Pascal Fua

  • Variational Quantum Boltzmann Machines

    Christa Zoufal;Aurélien Lucchi;Stefan Woerner

  • Are spatial and global constraints really necessary for segmentation

    Aurelien Lucchi;Yunpeng Li;Xavier Boix;Kevin Smith

  • A convolutional neural network for classifying cloud particles recorded by imaging probes

    Georgios Touloupas;Annika Lauber;Jan Henneberger;Alexander Beck

Frequent Co-Authors

Thomas Hofmann
Thomas Hofmann ETH Zurich
Pascal Fua
Pascal Fua École Polytechnique Fédérale de Lausanne
Jan Dirk Wegner
Jan Dirk Wegner University of Zurich
Andreas Krause
Andreas Krause ETH Zurich
Graham Knott
Graham Knott École Polytechnique Fédérale de Lausanne
Francis Bach
Francis Bach École Normale Supérieure
Sebastian Nowozin
Sebastian Nowozin Microsoft (United States)
Simon Lacoste-Julien
Simon Lacoste-Julien University of Montreal

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