World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
84
Citations
79064
World Ranking
818
National Ranking
445

Nitesh V. Chawla 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 Nitesh V. Chawla 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: 448 publications — 90th percentile

90% of scientists in this discipline score the same or lower.

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

Nitesh V. Chawla 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 Nitesh V. Chawla 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: 84 D-Index — 94th percentile

94% of scientists in this discipline score the same or lower.

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

Overview

Nitesh V. Chawla is affiliated with the University of Notre Dame in the United States. Their research primarily lies within the domain of Computer Science, with a notable focus on Artificial Intelligence, along with contributions in Statistical and Nonlinear Physics, Computer Vision and Pattern Recognition, Molecular Biology, and Information Systems.

Their main topics of work include:

  • Advanced Graph Neural Networks
  • Topic Modeling
  • Complex Network Analysis Techniques
  • Imbalanced Data Classification Techniques
  • Text and Document Classification Technologies
  • Recommender Systems and Techniques
  • Natural Language Processing Techniques

Recent papers authored or co-authored by Chawla reflect a range of interests within computational and machine learning fields. Selected works include:

  • "DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data" (2022), published in IEEE Transactions on Neural Networks and Learning Systems
  • "Few-Shot Knowledge Graph Completion" (2020), presented at the Proceedings of the AAAI Conference on Artificial Intelligence
  • "Graph Few-Shot Learning via Knowledge Transfer" (2020), presented at the Proceedings of the AAAI Conference on Artificial Intelligence
  • "On the use of real-world datasets for reaction yield prediction" (2023), published in Chemical Science
  • "Graph Barlow Twins: A self-supervised representation learning framework for graphs" (2022), published in Knowledge-Based Systems

Their frequent collaborators include researchers such as Chuxu Zhang, Nuno Moniz, Zhichun Guo, Xiangliang Zhang, and Meng Jiang.

Chawla's works have been published in venues with varying scopes and impact, notably:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Machine Learning
  • Frontiers in Big Data
  • Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence

Best Publications

  • SMOTE: synthetic minority over-sampling technique

    Nitesh V. Chawla;Kevin W. Bowyer;Lawrence O. Hall;W. Philip Kegelmeyer

  • SMOTE: Synthetic Minority Over-sampling Technique

    N. V. Chawla;K. W. Bowyer;L. O. Hall;W. P. Kegelmeyer

  • Editorial: special issue on learning from imbalanced data sets

    Nitesh V. Chawla;Nathalie Japkowicz;Aleksander Kotcz

  • SPECIAL ISSUE ON LEARNING FROM IMBALANCED DATA SETS

    N Chawla;N Japkowicz;A Kolcz

  • metapath2vec: Scalable Representation Learning for Heterogeneous Networks

    Yuxiao Dong;Nitesh V. Chawla;Ananthram Swami

  • SMOTEBoost: Improving Prediction of the Minority Class in Boosting

    Nitesh V. Chawla;Aleksandar Lazarevic;Lawrence O. Hall;Kevin W. Bowyer

  • SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary

    Alberto Fernández;Salvador García;Francisco Herrera;Nitesh V. Chawla

  • Data Mining for Imbalanced Datasets: An Overview

    Nitesh V. Chawla

  • Heterogeneous Graph Neural Network

    Chuxu Zhang;Dongjin Song;Chao Huang;Ananthram Swami

  • SVMs Modeling for Highly Imbalanced Classification

    Yuchun Tang;Yan-Qing Zhang;N.V. Chawla;S. Krasser

  • A unifying view on dataset shift in classification

    Jose G. Moreno-Torres;Troy Raeder;RocíO Alaiz-RodríGuez;Nitesh V. Chawla

  • New perspectives and methods in link prediction

    Ryan N. Lichtenwalter;Jake T. Lussier;Nitesh V. Chawla

  • A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data

    Chuxu Zhang;Dongjin Song;Yuncong Chen;Xinyang Feng

  • Bringing Big Data to Personalized Healthcare: A Patient-Centered Framework

    Nitesh V. Chawla;Darcy A. Davis

  • DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data.

    Damien Dablain;Bartosz Krawczyk;Nitesh V. Chawla

  • Inferring social status and rich club effects in enterprise communication networks.

    Yuxiao Dong;Jie Tang;Nitesh V. Chawla;Tiancheng Lou

  • Learning from streaming data with concept drift and imbalance: an overview

    T. Ryan Hoens;Robi Polikar;Nitesh V. Chawla

  • Learning Decision Trees for Unbalanced Data

    David A. Cieslak;Nitesh V. Chawla

  • Combating imbalance in network intrusion datasets

    D.A. Cieslak;N.V. Chawla;A. Striegel

  • Link Prediction and Recommendation across Heterogeneous Social Networks

    Yuxiao Dong;Jie Tang;Sen Wu;Jilei Tian

  • Big Data Opportunities and Challenges: Discussions from Data Analytics Perspectives [Discussion Forum]

    Zhi-Hua Zhou;Nitesh V. Chawla;Yaochu Jin;Graham J. Williams

  • Proceedings of the 2017 SIAM International Conference on Data Mining

    Nitesh Chawla;Wei Wang

  • Discovering Knowledge in Data: An Introduction to Data Mining

    Nitesh Chawla

Frequent Co-Authors

Yuxiao Dong
Yuxiao Dong Tsinghua University
Kevin W. Bowyer
Kevin W. Bowyer University of Notre Dame
Lawrence O. Hall
Lawrence O. Hall University of South Florida
Omar Lizardo
Omar Lizardo University of California, Los Angeles
Jie Tang
Jie Tang Tsinghua University
Meng Jiang
Meng Jiang University of Notre Dame
Dong Wang
Dong Wang Peking University
Chaoli Wang
Chaoli Wang University of Notre Dame
Douglas Thain
Douglas Thain University of Notre Dame
Ananthram Swami
Ananthram Swami United States Army Research Laboratory

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