World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
58
Citations
20236
World Ranking
3549
National Ranking
35

Gianluca Bontempi 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 Gianluca Bontempi 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: 281 publications — 70th percentile

70% of scientists in this discipline score the same or lower.

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

Gianluca Bontempi 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 Gianluca Bontempi 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: 58 D-Index — 75th percentile

75% of scientists in this discipline score the same or lower.

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

Overview

Gianluca Bontempi is affiliated with the Université Libre de Bruxelles in Belgium. The primary research focus encompasses computer science and engineering, with significant contributions in artificial intelligence and electrical and electronic engineering. Their work spans a range of interconnected subfields, including management science and operations research, molecular biology, and control and systems engineering.

The scientist's research covers various advanced topics, notably:

  • Imbalanced Data Classification Techniques
  • Machine Learning and Data Classification
  • Hand Gesture Recognition Systems
  • Epigenetics and DNA Methylation
  • Muscle activation and electromyography studies
  • Explainable Artificial Intelligence (XAI)
  • Customer churn and segmentation

Bontempi has collaborated frequently with several coauthors, including:

  • Bertrand Lebichot
  • Théo Verhelst
  • Gian Marco Paldino
  • Cédric Simar
  • Martin Colot

Publication venues where Bontempi's work commonly appears include:

  • arXiv (Cornell University)
  • SSRN Electronic Journal
  • IEEE Access
  • Expert Systems with Applications
  • Frontiers in Neuroscience

Recent papers authored by Bontempi provide insight into specific areas of investigation:

  • "Interpreting pathways to discover cancer driver genes with Moonlight," 2020, Nature Communications
  • "Incremental learning strategies for credit cards fraud detection," 2021, International Journal of Data Science and Analytics
  • "Probing the randomness of the local current distributions of 316 L stainless steel corrosion in NaCl solution," 2023, Corrosion Science
  • "Transfer Learning Strategies for Credit Card Fraud Detection," 2021, IEEE Access
  • "DAFT-E: Feature-Based Multivariate and Multi-Step-Ahead Wind Power Forecasting," 2021, IEEE Transactions on Sustainable Energy

Bontempi's scholarly output also includes book publications, notably a title published by Springer Science+Business Media:

  • Artificial Intelligence and Machine Learning, 2020

Best Publications

  • TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data.

    Antonio Colaprico;Tiago C. Silva;Catharina Olsen;Luciano Garofano

  • Machine learning strategies for time series forecasting

    Gianluca Bontempi;Souhaib Ben Taieb;Yann-Aël Le Borgne

  • minet : A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information

    Patrick E Meyer;Frédéric Lafitte;Gianluca Bontempi

  • A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition

    Souhaib Ben Taieb;Gianluca Bontempi;Amir F. Atiya;Antti Sorjamaa

  • Calibrating Probability with Undersampling for Unbalanced Classification

    Andrea Dal Pozzolo;Olivier Caelen;Reid Johnson;Gianluca Bontempi

  • Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy

    Andrea Dal Pozzolo;Giacomo Boracchi;Olivier Caelen;Cesare Alippi

  • New functionalities in the TCGAbiolinks package for the study and integration of cancer data from GDC and GTEX

    Mohamed Mounir;Marta Lucchetta;Tiago Henrique Da T.C. Silva;Catharina Olsen

  • Learned lessons in credit card fraud detection from a practitioner perspective

    Andrea Dal Pozzolo;Olivier Caelen;Yann-Aël Le Borgne;Serge Waterschoot

  • Information-theoretic inference of large transcriptional regulatory networks

    Patrick E. Meyer;Kevin Kontos;Frederic Lafitte;Gianluca Bontempi

  • Predicting prognosis using molecular profiling in estrogen receptor-positive breast cancer treated with tamoxifen.

    Sherene Loi;Benjamin Haibe-Kains;Christine Desmedt;Pratyaksha Wirapati

  • Combining unsupervised and supervised learning in credit card fraud detection

    Fabrizio Carcillo;Yann-Aël Le Borgne;Olivier Caelen;Yacine Kessaci

  • A Three-Gene Model to Robustly Identify Breast Cancer Molecular Subtypes

    Benjamin Haibe-Kains;Christine Desmedt;Sherene Loi;Aedin C. Culhane

  • Lazy learning for local modelling and control design

    Gianluca Bontempi;Mauro Birattari;Hugues Bersini

  • Information-Theoretic Feature Selection in Microarray Data Using Variable Complementarity

    P.E. Meyer;C. Schretter;G. Bontempi

  • Multiple-output modeling for multi-step-ahead time series forecasting

    Souhaib Ben Taieb;Antti Sorjamaa;Gianluca Bontempi

  • A comparative study of survival models for breast cancer prognostication based on microarray data

    B. Haibe-Kains;C. Desmedt;C. Sotiriou;G. Bontempi

  • mRMRe: an R package for parallelized mRMR ensemble feature selection.

    Nicolas De Jay;Simon Papillon-Cavanagh;Catharina Olsen;Nehme El-Hachem

  • Association between the PNPLA3 (rs738409 C>G) variant and hepatocellular carcinoma: Evidence from a meta‐analysis of individual participant data

    Eric Trepo;Pierre Nahon;Pierre Nahon;Gianluca Bontempi;Luca Valenti

  • A comprehensive overview of Infinium HumanMethylation450 data processing

    Sarah Dedeurwaerder;Matthieu Defrance;Martin Bizet;Emilie Calonne

  • Adaptive model selection for time series prediction in wireless sensor networks

    Yann-Aël Le Borgne;Silvia Santini;Gianluca Bontempi

  • CancerSubtypes: an R/Bioconductor package for molecular cancer subtype identification, validation and visualization.

    Taosheng Xu;Thuc Duy Le;Lin Liu;Ning Su

  • Association between the PNPLA3 (rs738409 C>G) variant and hepatocellular carcinoma: evidence from a meta-analysis of individual participant data

    Eric Trepo;Pierre Nahon;Gianluca Bontempi;Luca Valenti

Frequent Co-Authors

Benjamin Haibe-Kains
Benjamin Haibe-Kains Princess Margaret Cancer Centre
Mauro Birattari
Mauro Birattari Université Libre de Bruxelles
Hugues Bersini
Hugues Bersini Université Libre de Bruxelles
Christos Sotiriou
Christos Sotiriou Université Libre de Bruxelles
Houtan Noushmehr
Houtan Noushmehr Henry Ford Health System
Robert Kiss
Robert Kiss Université Libre de Bruxelles
John Quackenbush
John Quackenbush Harvard University
Sherene Loi
Sherene Loi Peter MacCallum Cancer Centre
Marc Buyse
Marc Buyse Hasselt University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring online degrees in Computer Science opens up flexible pathways for anyone looking to advance their education and career. Many students and professionals are now considering an associate degree online as a practical starting point. These programs allow you to gain foundational skills affordably and quickly.

For those aiming for advanced expertise, pursuing one of the most useful graduate degrees can greatly enhance your prospects. Computer Science, Data Science, and Cybersecurity are just a few fields where demand continues to rise.

Time-conscious learners may be interested in the shortest online masters degree options. These accelerated programs let you earn a recognized qualification in less time, so you can move forward in your career faster.

Budget is also a key consideration. You can find a wide range of cheap online college classes that deliver quality education without breaking the bank. These affordable choices make it possible to upskill or reskill at your own pace.

Best Scientists Citing Gianluca Bontempi

Trending Scientists

Recently Published Articles