World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
58
Citations
17202
World Ranking
3570
National Ranking
107

Marcello La Rosa 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 Marcello La Rosa 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: 220 publications — 53rd percentile

53% of scientists in this discipline score the same or lower.

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

Marcello La Rosa 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 Marcello La Rosa 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

Marcello La Rosa is affiliated with the University of Melbourne in Australia. Their research spans key areas in Business, Management and Accounting as well as Computer Science, with substantial contributions in subfields such as Management Information Systems, Information Systems, Management Science and Operations Research, Artificial Intelligence, and Industrial and Manufacturing Engineering.

The primary research topics associated with Marcello La Rosa include Business Process Modeling and Analysis, Service-Oriented Architecture and Web Services, Data Quality and Management, Robotic Process Automation Applications, Big Data and Business Intelligence, Outsourcing and Supply Chain Management, and Time Series Analysis and Forecasting.

Notable recent publications include:

  • "Robotic Process Mining: Vision and Challenges" (2020), published in Business & Information Systems Engineering
  • "AI-augmented Business Process Management Systems: A Research Manifesto" (2023), published in ACM Transactions on Management Information Systems
  • "Opportunities and Challenges for Process Mining in Organizations: Results of a Delphi Study" (2021), published in Business & Information Systems Engineering
  • "Seven Paradoxes of Business Process Management in a Hyper-Connected World" (2020), published in Business & Information Systems Engineering
  • "Process Mining Meets Causal Machine Learning: Discovering Causal Rules from Event Logs" (2021), published in Minerva Access (University of Melbourne)

Frequent co-authors collaborating with Marcello La Rosa include Marlon Dumas, Artem Polyvyanyy, Adriano Augusto, Fabrizio Maria Maggi, and Farbod Taymouri.

Publication venues preferred by Marcello La Rosa commonly feature sources such as arXiv (Cornell University), Information Systems, Business & Information Systems Engineering, Zenodo (CERN European Organization for Nuclear Research), and Lecture Notes in Computer Science.

Their book publication record includes a work titled Advanced Information Systems Engineering (2021), released by Springer Science+Business Media.

Best Publications

  • Fundamentals of Business Process Management

    Marlon Dumas;Marcello La Rosa;Jan Mendling;Hajo A. Reijers

  • Process Mining Manifesto

    Wil van der Aalst;Wil van der Aalst;Arya Adriansyah;Ana Karla Alves de Medeiros;Franco Arcieri

  • Blockchains for Business Process Management - Challenges and Opportunities

    Jan Mendling;Ingo Weber;Wil Van Der Aalst;Jan Vom Brocke

  • Predictive Business Process Monitoring with LSTM Neural Networks

    Niek Tax;Ilya Verenich;Marcello La Rosa;Marlon Dumas

  • Automated Discovery of Process Models from Event Logs: Review and Benchmark

    Adriano Augusto;Raffaele Conforti;Marlon Dumas;Marcello La Rosa

  • Outcome-Oriented Predictive Process Monitoring: Review and Benchmark

    Irene Teinemaa;Marlon Dumas;Marcello La Rosa;Fabrizio Maria Maggi

  • Business Process Variability Modeling: A Survey

    Marcello La Rosa;Wil M. P. Van Der Aalst;Marlon Dumas;Fredrik P. Milani

  • CONFIGURABLE WORKFLOW MODELS

    F Florian Gottschalk;Wmp Wil van der Aalst;MH Monique Jansen-Vullers;Marcello La Rosa

  • The Architecture

    Unknown

  • APROMORE : an advanced process model repository

    Marcello La Rosa;Hajo A. Reijers;Wil M.P. van der Aalst;Remco M. Dijkman

  • Configurable multi-perspective business process models

    Marcello La Rosa;Marlon Dumas;Arthur H. M. ter Hofstede;Jan Mendling

  • Business Process Model Merging: An Approach to Business Process Consolidation

    Marcello La Rosa;Marlon Dumas;Reina Uba;Remco Dijkman

  • Split miner: automated discovery of accurate and simple business process models from event logs

    Adriano Augusto;Adriano Augusto;Raffaele Conforti;Marlon Dumas;Marcello La Rosa

  • Editorial: Managing large collections of business process models-Current techniques and challenges

    Remco Dijkman;Marcello La Rosa;Hajo A. Reijers

  • Filtering Out Infrequent Behavior from Business Process Event Logs

    Raffaele Conforti;Marcello La Rosa;Arthur H.M. ter Hofstede

  • Questionnaire-based variability modeling for system configuration

    Marcello La Rosa;Wil M. P. van der Aalst;Wil M. P. van der Aalst;Marlon Dumas;Marlon Dumas;Arthur H. M. ter Hofstede

  • A recommendation system for predicting risks across multiple business process instances

    Raffaele Conforti;Massimiliano de Leoni;Massimiliano de Leoni;Marcello La Rosa;Marcello La Rosa;Wil M.P. van der Aalst;Wil M.P. van der Aalst

  • Process-Aware Information Systems

    Marlon Dumas;Marcello La Rosa;Jan Mendling;Hajo A. Reijers

  • Merging business process models

    Marcello La Rosa;Marlon Dumas;Reina Uba;Remco Dijkman

  • Preserving correctness during business process model configuration

    Wil M. P. van der Aalst;Marlon Dumas;Florian Gottschalk;Arthur H. M. ter Hofstede

  • Survey and Cross-benchmark Comparison of Remaining Time Prediction Methods in Business Process Monitoring

    Ilya Verenich;Marlon Dumas;Marcello La Rosa;Fabrizio Maria Maggi

  • Business Process Management

    h. c. Wil M. P. van der Aalst;Marcello La Rosa;Marcello La Rosa;Flávia Maria Santoro

  • Blockchains for Business Process Management - Challenges and Opportunities

    Jan Mendling;Ingo Weber;Wil van der Aalst;Jan vom Brocke

Frequent Co-Authors

Marlon Dumas
Marlon Dumas University of Tartu
Arthur H. M. ter Hofstede
Arthur H. M. ter Hofstede Queensland University of Technology
Fabrizio Maria Maggi
Fabrizio Maria Maggi Free University of Bozen-Bolzano
Jan Mendling
Jan Mendling Humboldt-Universität zu Berlin
Wil M. P. van der Aalst
Wil M. P. van der Aalst RWTH Aachen University
Artem Polyvyanyy
Artem Polyvyanyy University of Melbourne
Michael Adams
Michael Adams University of Birmingham
Giancarlo Fortino
Giancarlo Fortino University of Calabria
Michael Rosemann
Michael Rosemann Queensland University of Technology
Jianmin Wang
Jianmin Wang Tsinghua University

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