World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
65
Citations
29592
World Ranking
2393
National Ranking
1195

Julian McAuley 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 Julian McAuley 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: 251 publications — 63rd percentile

63% of scientists in this discipline score the same or lower.

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

Julian McAuley 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 Julian McAuley 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: 65 D-Index — 83rd percentile

83% of scientists in this discipline score the same or lower.

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

Overview

Julian McAuley is affiliated with the University of California, San Diego in the United States. Their research primarily lies within the field of Computer Science, with a strong focus on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Signal Processing, and Management Science and Operations Research.

Their scholarly output spans topics such as Topic Modeling, Natural Language Processing Techniques, Recommender Systems and Techniques, Music and Audio Processing, Multimodal Machine Learning Applications, Music Technology and Sound Studies, and Domain Adaptation and Few-Shot Learning.

Some recent publications include:

  • Intent Contrastive Learning for Sequential Recommendation, 2022, Proceedings of the ACM Web Conference 2022
  • ReZero is All You Need: Fast Convergence at Large Depth, 2020, arXiv (Cornell University)
  • RadBERT: Adapting Transformer-based Language Models to Radiology, 2022, Radiology Artificial Intelligence
  • Deep reinforcement learning in recommender systems: A survey and new perspectives, 2023, Knowledge-Based Systems
  • What's in a Name? Understanding the Interplay between Titles, Content, and Communities in Social Media, 2021, Proceedings of the International AAAI Conference on Web and Social Media

Frequently collaborating researchers include Taylor Berg-Kirkpatrick, Lina Yao, Zexue He, Canwen Xu, and Zhankui He, reflecting sustained partnerships in multiple projects and publications.

Julian McAuley has contributed to several renowned venues such as:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Zenodo (CERN European Organization for Nuclear Research)
  • Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  • Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Additionally, McAuley has published a book titled Personalized Machine Learning in 2022 through Cambridge University Press.

Best Publications

  • Self-Attentive Sequential Recommendation

    Wang-Cheng Kang;Julian McAuley

  • Image-Based Recommendations on Styles and Substitutes

    Julian McAuley;Christopher Targett;Qinfeng Shi;Anton van den Hengel

  • Learning to Discover Social Circles in Ego Networks

    Jure Leskovec;Julian J. Mcauley

  • Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering

    Ruining He;Julian McAuley

  • Hidden factors and hidden topics: understanding rating dimensions with review text

    Julian McAuley;Jure Leskovec

  • Community Detection in Networks with Node Attributes

    Jaewon Yang;Julian McAuley;Jure Leskovec

  • Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects.

    Jianmo Ni;Jiacheng Li;Julian J. McAuley

  • VBPR: visual Bayesian Personalized Ranking from implicit feedback

    Ruining He;Julian McAuley

  • Inferring Networks of Substitutable and Complementary Products

    Julian McAuley;Rahul Pandey;Jure Leskovec

  • Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation

    Ruining He;Julian McAuley

  • From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews

    Julian John McAuley;Jure Leskovec

  • Learning Graph Matching

    T.S. Caetano;J.J. McAuley;Li Cheng;Q.V. Le

  • Time Interval Aware Self-Attention for Sequential Recommendation

    Jiacheng Li;Yujie Wang;Julian McAuley

  • Leveraging Social Connections to Improve Personalized Ranking for Collaborative Filtering

    Tong Zhao;Julian McAuley;Irwin King

  • Translation-based Recommendation

    Ruining He;Wang-Cheng Kang;Julian McAuley

  • Discovering social circles in ego networks

    Julian Mcauley;Jure Leskovec

  • Learning Visual Clothing Style with Heterogeneous Dyadic Co-Occurrences

    Andreas Veit;Balazs Kovacs;Sean Bell;Julian McAuley

  • Learning Attitudes and Attributes from Multi-aspect Reviews

    Julian McAuley;Jure Leskovec;Dan Jurafsky

  • Visually-Aware Fashion Recommendation and Design with Generative Image Models

    Wang-Cheng Kang;Chen Fang;Zhaowen Wang;Julian McAuley

  • Adversarial Audio Synthesis

    Chris Donahue;Julian J. McAuley;Miller S. Puckette

  • BERT Loses Patience: Fast and Robust Inference with Early Exit

    Wangchunshu Zhou;Canwen Xu;Tao Ge;Julian J. McAuley

  • Translation-based Recommendation: A Scalable Method for Modeling Sequential Behavior.

    Ruining He;Wang-Cheng Kang;Julian J. McAuley

Frequent Co-Authors

Jure Leskovec
Jure Leskovec Stanford University
Zachary C. Lipton
Zachary C. Lipton Carnegie Mellon University
Garrison W. Cottrell
Garrison W. Cottrell University of California, San Diego
Farinaz Koushanfar
Farinaz Koushanfar University of California, San Diego
Shlomo Dubnov
Shlomo Dubnov University of California, San Diego
Alexander J. Smola
Alexander J. Smola Amazon (United States)
Qinfeng Shi
Qinfeng Shi University of Adelaide
Chun-Nan Hsu
Chun-Nan Hsu University of California, San Diego
Anton van den Hengel
Anton van den Hengel University of Adelaide

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