World's Best Scientists 2026 revealed!
Award Badge
Computer Science
Slovenia
2026

D-Index & Metrics

Computer Science

D-Index
57
Citations
15925
World Ranking
3780
National Ranking
2

Nada Lavrač 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 Nada Lavrač 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: 353 publications — 82nd percentile

82% of scientists in this discipline score the same or lower.

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

Nada Lavrač 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 Nada Lavrač 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: 57 D-Index — 74th percentile

74% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2026 - Research.com Computer Science in Slovenia Leader Award
  • 2025 - Research.com Computer Science in Slovenia Leader Award
  • 2023 - Research.com Computer Science in Slovenia Leader Award
  • 2022 - Research.com Computer Science in Slovenia Leader Award

Overview

Nada Lavrač is affiliated with the Jozef Stefan Institute in Slovenia. Their research spans multiple fields, with a significant focus on computer science and biochemistry, genetics, and molecular biology. Their publication record reflects a diverse engagement with topics across these disciplines.

The main fields of study associated with Nada Lavrač include:

  • Computer Science
  • Biochemistry, Genetics and Molecular Biology

Their work extends into several subfields, notably:

  • Artificial Intelligence
  • Molecular Biology
  • Plant Science
  • Statistical and Nonlinear Physics
  • Computational Theory and Mathematics

Key research topics covered by their publications include:

  • Topic Modeling
  • Biomedical Text Mining and Ontologies
  • Advanced Text Analysis Techniques
  • Natural Language Processing Techniques
  • Bioinformatics and Genomic Networks
  • Machine Learning and Data Classification
  • Semantic Web and Ontologies

Nada Lavrač has published extensively in prominent venues, with frequent publications appearing in:

  • arXiv (Cornell University)
  • Machine Learning
  • Zenodo (CERN European Organization for Nuclear Research)
  • Machine Learning and Knowledge Extraction
  • IEEE Access

Notable papers authored or co-authored by Nada Lavrač include:

  • Advancing manufacturing systems with big-data analytics: A conceptual framework (2020) - International Journal of Computer Integrated Manufacturing
  • LemmaGen: Multilingual Lemmatisation with Induced Ripple-Down Rules (2020) - TUGraz OPEN Library (Graz University of Technology)
  • tax2vec: Constructing Interpretable Features from Taxonomies for Short Text Classification (2020) - Computer Speech & Language
  • Feature Importance Estimation with Self-Attention Networks (2020) - arXiv (Cornell University)
  • autoBOT: evolving neuro-symbolic representations for explainable low resource text classification (2021) - Machine Learning

Frequent collaborators include:

  • Blaž Škrlj
  • Senja Pollak
  • Boshko Koloski
  • Bojan Cestnik
  • Matej Martinc

Best Publications

  • The multi-purpose incremental learning system AQ15 and its testing application to three medical domains

    Ryszard S. Michalski;Igor Mozetic;Jiarong Hong;Nada Lavrac

  • Inductive Logic Programming: Techniques and Applications

    Nada Lavrac;Saso Dzeroski

  • Relational Data Mining

    Saso Dzeroski;Nada Lavrac

  • Rule Evaluation Measures: A Unifying View

    Nada Lavrac;Peter A. Flach;Blaz Zupan

  • Foundations of Rule Learning

    Johannes Frnkranz;Dragan Gamberger;Nada Lavrac

  • Selected Techniques for Data Mining in Medicine

    Nada Lavrač

  • Subgroup Discovery with CN2-SD

    Nada Lavrač;Branko Kavšek;Peter Flach;Ljupčo Todorovski

  • Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining

    Petra Kralj Novak;Nada Lavrač;Geoffrey I. Webb

  • Propositionalization approaches to relational data mining

    Stefan Kramer;Nada Lavrač;Peter Flach

  • Stream-based active learning for sentiment analysis in the financial domain

    Jasmina Smailović;Miha Grčar;Nada Lavrač;Martin Žnidaršič

  • Learning nonrecursive definitions of relations with LINUS

    Nada Lavrač;Sašo Džeroski;Marko Grobelnik

  • Expert-guided subgroup discovery: methodology and application

    Dragan Gamberger;Nada Lavrac

  • APRIORI-SD: ADAPTING ASSOCIATION RULE LEARNING TO SUBGROUP DISCOVERY

    Branko Kavšek;Nada Lavrač;Viktor Jovanoski

  • Kardio : A Study in Deep and Qualitative Knowledge for Expert Systems

    Ivan Bratko;Igor Mozetič;Nada Lavrač

  • The AQ15 Inductive Learning System: An Overview and Experiments

    Ryszard S. Michalski;Igor Mozetic;Jiarong Hong;Nada Lavrac

  • Experiments with Noise Filtering in a Medical Domain

    Dragan Gamberger;Nada Lavrac;Ciril Groselj

  • A Rule based Approach to Word Lemmatization

    Joël Plisson;Nada Lavrac;Dunja Mladenic

  • Knowledge Discovery in Databases: PKDD 2003

    Nada Lavrač;Dragan Gamberger;Ljupčo Todorovski;Hendrik Blockeel

  • Comparative Evaluation of Approaches to Propositionalization

    Mark-A. Krogel;Simon Rawles;Filip Železný;Filip Železný;Peter A. Flach

  • Noise detection and elimination in data preprocessing: Experiments in medical domains

    Dragan Gamberger;Nada Lavrac;Saso Dzeroski

Frequent Co-Authors

Kristina Gruden
Kristina Gruden National Institute of Biology
Peter A. Flach
Peter A. Flach University of Bristol
Johannes Fürnkranz
Johannes Fürnkranz Johannes Kepler University of Linz
Sašo Džeroski
Sašo Džeroski Jožef Stefan Institute
Dunja Mladenic
Dunja Mladenic Jožef Stefan Institute
Hannu Toivonen
Hannu Toivonen University of Helsinki
Jakub Tolar
Jakub Tolar University of Minnesota
Geraint A. Wiggins
Geraint A. Wiggins Vrije Universiteit Brussel
Stefan Wrobel
Stefan Wrobel University of Bonn
Luc De Raedt
Luc De Raedt KU Leuven

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 learning can open up flexible options for students interested in computer science and related fields. Many universities in the USA now offer programs that range from associate to doctoral degrees, catering to different career goals and timelines.

Students seeking higher qualifications can look into the cheapest online PhD programs in USA. These programs are ideal for those wanting to pursue advanced research or academic careers without incurring excessive debt. For professionals who want to progress quickly, shortest online EdD programs offer fast-track pathways to leadership roles in education or technology.

If you’re looking to enter the workforce quickly, consider a 6 month degree course. These accelerated courses can launch your tech or IT career in less than a year. Additionally, complementing a computer science background with a business education can boost your career potential. Many institutions offer affordable options, such as the business degree online.

Choosing the right program depends on your goals, budget, and desired timeline—making online education a versatile solution.

Best Scientists Citing Nada Lavrač

Trending Scientists

Recently Published Articles