World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
41
Citations
12423
World Ranking
8614
National Ranking
3691

Yanjun Qi 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 Yanjun Qi 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: 131 publications — 19th percentile

19% of scientists in this discipline score the same or lower.

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

Yanjun Qi 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 Yanjun Qi 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: 41 D-Index — 40th percentile

40% of scientists in this discipline score the same or lower.

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

Overview

Yanjun Qi is affiliated with the University of Virginia in the United States and conducts research primarily in the field of Computer Science. Within this broad domain, their work focuses extensively on Artificial Intelligence, with a significant number of publications also spanning Molecular Biology, Computer Vision and Pattern Recognition, Computer Networks and Communications, and Infectious Diseases.

Their research topics cover a variety of areas, including:

  • Topic Modeling
  • Domain Adaptation and Few-Shot Learning
  • Adversarial Robustness in Machine Learning
  • Machine Learning and Data Classification
  • Multimodal Machine Learning Applications
  • Explainable Artificial Intelligence (XAI)
  • Genomics and Phylogenetic Studies

Yanjun Qi has contributed to multiple publication venues, with the majority of work appearing in:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Bioinformatics
  • mSystems

Recent research papers authored or co-authored by Yanjun Qi include:

  • Long-Range Transformers for Dynamic Spatiotemporal Forecasting (2021), published in arXiv (Cornell University)
  • Forecasting Cloud Application Workloads With CloudInsight for Predictive Resource Management (2020), published in IEEE Transactions on Cloud Computing
  • TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP (2020), published in arXiv (Cornell University)
  • Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning (2021), published in Proceedings of the AAAI Conference on Artificial Intelligence
  • Pathogenesis, Symptomatology, and Transmission of SARS-CoV-2 through Analysis of Viral Genomics and Structure (2021), published in arXiv (Cornell University)

The scientist frequently collaborates with other researchers, including:

  • Arshdeep Sekhon
  • Jake Grigsby
  • Vicente Ordóñez
  • Jack Lanchantin
  • Zhe Wang

Best Publications

  • Opportunities and obstacles for deep learning in biology and medicine.

    Travers Ching;Daniel S. Himmelstein;Brett K. Beaulieu-Jones;Alexandr A. Kalinin

  • Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks.

    Weilin Xu;David Evans;Yanjun Qi

  • Random Forest for Bioinformatics

    Yanjun Qi

  • Black-Box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers

    Ji Gao;Jack Lanchantin;Mary Lou Soffa;Yanjun Qi

  • TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP.

    John X. Morris;Eli Lifland;Jin Yong Yoo;Jake Grigsby

  • Evaluation of different biological data and computational classification methods for use in protein interaction prediction

    Yanjun Qi;Ziv Bar-Joseph;Judith Klein-Seetharaman;Judith Klein-Seetharaman

  • General Multi-label Image Classification with Transformers

    Jack Lanchantin;Tianlu Wang;Vicente Ordonez;Yanjun Qi

  • DeepChrome: Deep-learning for predicting gene expression from histone modifications

    Ritambhara Singh;Jack Lanchantin;Gabriel Robins;Yanjun Qi

  • A critical assessment of Mus musculus gene function prediction using integrated genomic evidence.

    Lourdes Pena-Castillo;Murat Tasan;Chad L Myers;Hyunju Lee

  • Automatically Evading Classifiers: A Case Study on PDF Malware Classifiers.

    Weilin Xu;Yanjun Qi;David Evans

  • Random forest similarity for protein-protein interaction prediction from multiple sources.

    Yanjun Qi;Judith Klein-Seetharaman;Ziv Bar-Joseph

  • Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning.

    Paola Cascante-Bonilla;Fuwen Tan;Yanjun Qi;Vicente Ordonez

  • Systems and methods for semi-supervised relationship extraction

    Yanjun Qi;Xia Ning;Pavel Kuksa;Bing Bai

  • Cas9-chromatin binding information enables more accurate CRISPR off-target prediction

    Ritambhara Singh;Cem Kuscu;Aaron Quinlan;Yanjun Qi

  • Sentiment classification based on supervised latent n-gram analysis

    Dmitriy Bespalov;Bing Bai;Yanjun Qi;Ali Shokoufandeh

  • Prediction of interactions between HIV-1 and human proteins by information integration.

    Oznur Tastan;Yanjun Qi;Jaime G. Carbonell;Judith Klein-Seetharaman

  • Protein complex identification by supervised graph local clustering

    Yanjun Qi;Fernanda Balem;Christos Faloutsos;Judith Klein-Seetharaman

  • Semi-supervised multi-task learning for predicting interactions between HIV-1 and human proteins

    Yanjun Qi;Oznur Tastan;Jaime G. Carbonell;Judith Klein-Seetharaman

  • DEEP MOTIF DASHBOARD: VISUALIZING AND UNDERSTANDING GENOMIC SEQUENCES USING DEEP NEURAL NETWORKS.

    Jack Lanchantin;Ritambhara Singh;Beilun Wang;Yanjun Qi

  • Learning to rank with (a lot of) word features

    Bing Bai;Jason Weston;David Grangier;Ronan Collobert

Frequent Co-Authors

Judith Klein-Seetharaman
Judith Klein-Seetharaman Arizona State University
Jason Weston
Jason Weston Facebook (United States)
Ziv Bar-Joseph
Ziv Bar-Joseph Carnegie Mellon University
Koray Kavukcuoglu
Koray Kavukcuoglu DeepMind (United Kingdom)
Ronan Collobert
Ronan Collobert Facebook (United States)
Casey S. Greene
Casey S. Greene University of Colorado Denver
Jaime G. Carbonell
Jaime G. Carbonell Carnegie Mellon University
Alexander G. Hauptmann
Alexander G. Hauptmann Carnegie Mellon University
Marty Humphrey
Marty Humphrey University of Virginia
John Lach
John Lach George Washington University

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