World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
48
Citations
13778
World Ranking
6053
National Ranking
97

Mykola Pechenizkiy 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 Mykola Pechenizkiy 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: 385 publications — 86th percentile

86% of scientists in this discipline score the same or lower.

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

Mykola Pechenizkiy 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 Mykola Pechenizkiy 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: 48 D-Index — 58th percentile

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

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

Overview

Mykola Pechenizkiy is affiliated with Eindhoven University of Technology in the Netherlands. Their research contributions primarily lie within the broad field of Computer Science, with a significant focus on Artificial Intelligence. They have published extensively in areas including Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Management Science and Operations Research, and Information Systems.

The scientist's work covers several key topics, notably Domain Adaptation and Few-Shot Learning, Advanced Neural Network Applications, Adversarial Robustness in Machine Learning, Advanced Graph Neural Networks, Anomaly Detection Techniques and Applications, Complex Network Analysis Techniques, and Machine Learning and Data Classification.

Frequent publication venues where their research appears include arXiv (Cornell University), Machine Learning, Data Mining and Knowledge Discovery, Proceedings of the AAAI Conference on Artificial Intelligence, and Neural Computing and Applications.

Mykola Pechenizkiy has collaborated extensively with several researchers. The most frequent coauthors are Decebal Constantin Mocanu, Yulong Pei, Tianjin Huang, Meng Fang, and Vlado Menkovski.

Selected recent research papers include:

  • Bridging learning sciences, machine learning and affective computing for understanding cognition and affect in collaborative learning (2020), British Journal of Educational Technology
  • More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity (2022), arXiv (Cornell University)
  • Ensuring cybersecurity of smart grid against data integrity attacks under concept drift (2020), International Journal of Electrical Power & Energy Systems
  • EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features (2020), Brain Informatics
  • ResGCN: attention-based deep residual modeling for anomaly detection on attributed networks (2021), Machine Learning

Best Publications

  • A survey on concept drift adaptation

    João Gama;Indrė Žliobaitė;Albert Bifet;Mykola Pechenizkiy

  • Handbook of Educational Data Mining

    Cristobal Romero;Sebastian Ventura;Mykola Pechenizkiy;Ryan S.J.d. Baker

  • Predicting Students Drop Out: A Case Study

    GW Gerben Dekker;M Mykola Pechenizkiy;JM Jan Vleeshouwers

  • Building Classifiers with Independency Constraints

    Toon Calders;Faisal Kamiran;Mykola Pechenizkiy

  • An Overview of Concept Drift Applications

    Indrė Žliobaitė;Indrė Žliobaitė;Indrė Žliobaitė;Mykola Pechenizkiy;João Gama

  • Discrimination Aware Decision Tree Learning

    Faisal Kamiran;Toon Calders;Mykola Pechenizkiy

  • Diversity in search strategies for ensemble feature selection

    Alexey Tsymbal;Mykola Pechenizkiy;Pádraig Cunningham

  • What's Your Current Stress Level? Detection of Stress Patterns from GSR Sensor Data

    Jorn Bakker;Mykola Pechenizkiy;Natalia Sidorova

  • Dynamic integration of classifiers for handling concept drift

    Alexey Tsymbal;Mykola Pechenizkiy;Pádraig Cunningham;Seppo Puuronen

  • AH 12 years later: a comprehensive survey of adaptive hypermedia methods and techniques

    Evgeny Knutov;Paul De Bra;Mykola Pechenizkiy

  • Stress detection from speech and Galvanic Skin Response signals

    Hindra Kurniawan;Alexandr V. Maslov;Mykola Pechenizkiy

  • Handling concept drift in process mining

    R. P. Jagadeesh Chandra Bose;Wil M. P. van der Aalst;Indre Žliobaite;Mykola Pechenizkiy

  • Dealing With Concept Drifts in Process Mining

    R. P. Jagadeesh Chandra Bose;Wil M. P. van der Aalst;Indre Zliobaite;Mykola Pechenizkiy

  • Feedback Loop and Bias Amplification in Recommender Systems

    Masoud Mansoury;Himan Abdollahpouri;Mykola Pechenizkiy;Bamshad Mobasher

  • More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity

    Unknown

  • Introduction to the special section on educational data mining

    Toon Calders;Mykola Pechenizkiy

  • Class Noise and Supervised Learning in Medical Domains: The Effect of Feature Extraction

    M. Pechenizkiy;A. Tsymbal;S. Puuronen;O. Pechenizkiy

  • Dynamic integration with random forests

    Alexey Tsymbal;Mykola Pechenizkiy;Padraig Cunningham

  • Bridging learning sciences, machine learning and affective computing for understanding cognition and affect in collaborative learning

    Sanna Järvelä;Dragan Gašević;Tapio Seppänen;Mykola Pechenizkiy

  • Feedback adaptation in web-based learning systems

    Ekaterina Vasilyeva;Seppo Puuronen;Mykola Pechenizkiy;Pekka Rasanen

  • Stess@Work: from measuring stress to its understanding, prediction and handling with personalized coaching

    Jorn Bakker;Leszek Holenderski;Rafal Kocielnik;Mykola Pechenizkiy

  • Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems

    Pedro Pereira Rodrigues;Mykola Pechenizkiy;João Gama;Faculdade de Economia

  • Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training

    Shiwei Liu;Lu Yin;Decebal Constantin Mocanu;Mykola Pechenizkiy

Frequent Co-Authors

Toon Calders
Toon Calders University of Antwerp
Paul De Bra
Paul De Bra Eindhoven University of Technology
Sebastián Ventura
Sebastián Ventura University of Córdoba
Bamshad Mobasher
Bamshad Mobasher DePaul University
Cristóbal Romero
Cristóbal Romero University of Córdoba
João Gama
João Gama University of Porto
Pádraig Cunningham
Pádraig Cunningham University College Dublin
Robin Burke
Robin Burke University of Colorado Boulder
Mohamed Medhat Gaber
Mohamed Medhat Gaber Birmingham City University
Dragan Gasevic
Dragan Gasevic Monash University

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