World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
48
Citations
15023
World Ranking
6037
National Ranking
2719

Lichan Hong 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 Lichan Hong 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 125 publications — 17th percentile

17% of scientists in this discipline score the same or lower.

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

Lichan Hong 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 Lichan Hong sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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

Lichan Hong is affiliated with Google in the United States and works primarily in the field of Computer Science. Their research encompasses key areas including Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Management Science and Operations Research, and General Health Professions. The predominant focus lies on Artificial Intelligence and Information Systems, constituting a significant portion of their scholarly output.

Their work addresses topics such as Recommender Systems and Techniques, Topic Modeling, Advanced Bandit Algorithms Research, Advanced Graph Neural Networks, Domain Adaptation and Few-Shot Learning, Multimodal Machine Learning Applications, and Data Stream Mining Techniques.

They have published extensively in venues that include:

  • arXiv (Cornell University)
  • Proceedings of the International AAAI Conference on Web and Social Media
  • Companion Proceedings of the Web Conference 2020
  • Proceedings of the ACM Web Conference 2022

Recent notable papers include:

  • "Language Matters In Twitter: A Large Scale Study" (2021), published in Proceedings of the International AAAI Conference on Web and Social Media
  • "Is Twitter a Good Place for Asking Questions? A Characterization Study" (2021), published in Proceedings of the International AAAI Conference on Web and Social Media
  • "Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations" (2020), published in Companion Proceedings of the Web Conference 2020
  • "Learning Multi-granular Quantized Embeddings for Large-Vocab Categorical Features in Recommender Systems" (2020), published in Companion Proceedings of the Web Conference 2020
  • "Distributionally-robust Recommendations for Improving Worst-case User Experience" (2022), published in Proceedings of the ACM Web Conference 2022

Frequent collaborators include:

  • Ed H.
  • Xinyang Yi
  • Derek Zhiyuan Cheng
  • Maheswaran Sathiamoorthy
  • Wang-Cheng Kang

Best Publications

  • Wide & Deep Learning for Recommender Systems

    Heng-Tze Cheng;Levent Koc;Jeremiah Harmsen;Tal Shaked

  • Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network

    Bongwon Suh;Lichan Hong;Peter Pirolli;Ed H. Chi

  • Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts

    Jiaqi Ma;Zhe Zhao;Xinyang Yi;Jilin Chen

  • Tweets from Justin Bieber's heart: the dynamics of the location field in user profiles

    Brent Hecht;Lichan Hong;Bongwon Suh;Ed H. Chi

  • Virtual voyage: interactive navigation in the human colon

    Lichan Hong;Shigeru Muraki;Arie Kaufman;Dirk Bartz

  • DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems

    Ruoxi Wang;Rakesh Shivanna;Derek Z. Cheng;Sagar Jain

  • Generation of transfer functions with stochastic search techniques

    Taosong He;Lichan Hong;Arie Kaufman;Hanspeter Pfister

  • 3D virtual colonoscopy

    Lichan Hong;A. Kaufman;Yi-Chih Wei;A. Viswambharan

  • Fairness in Recommendation Ranking through Pairwise Comparisons

    Alex Beutel;Jilin Chen;Tulsee Doshi;Hai Qian

  • Method and system for providing search based on topic

    Stuart K Card;Lichan Hong;Peter L Pirolli;Mark J Stefik

  • DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems

    Ruoxi Wang;Rakesh Shivanna;Derek Cheng;Sagar Jain

  • Recommending what video to watch next: a multitask ranking system

    Zhe Zhao;Lichan Hong;Li Wei;Jilin Chen

  • Language Matters In Twitter: A Large Scale Study

    Lichan Hong;Gregorio Convertino;Ed H. Chi

  • Automatic centerline extraction for virtual colonoscopy

    Ming Wan;Zhengrong Liang;Qi Ke;Lichan Hong

  • Eddi: interactive topic-based browsing of social status streams

    Michael S. Bernstein;Bongwon Suh;Lichan Hong;Jilin Chen

  • Self-supervised Learning for Large-scale Item Recommendations

    Tiansheng Yao;Xinyang Yi;Derek Zhiyuan Cheng;Felix Yu

  • Voxel based object simplification

    Taosong He;Lichan Hong;A. Kaufman;A. Varshney

  • Sampling-bias-corrected neural modeling for large corpus item recommendations

    Xinyang Yi;Ji Yang;Lichan Hong;Derek Zhiyuan Cheng

  • Systems and methods for turning pages in a three-dimensional electronic document

    Lichan Hong;Stuart K. Card;Jindong Chen

  • Improving User Topic Interest Profiles by Behavior Factorization

    Zhe Zhao;Zhiyuan Cheng;Lichan Hong;Ed H. Chi

  • Verfahren zur automatischen konzeptuellen Hervorhebung in electronischen Text

    Ed H. Chi;Lichan Hong;Stuart K. Card

  • Controlled topology simplification

    Taosong He;Lichan Hong;A. Varshney;S.W. Wang

  • Is Twitter a Good Place for Asking Questions? A Characterization Study.

    Sharoda A. Paul;Lichan Hong;Ed H. Chi

Frequent Co-Authors

Ed H. Chi
Ed H. Chi Google (United States)
Arie E. Kaufman
Arie E. Kaufman Stony Brook University
Stuart K. Card
Stuart K. Card Stanford University
Zhengrong Liang
Zhengrong Liang Stony Brook University
Peter Pirolli
Peter Pirolli Florida Institute for Human and Machine Cognition
Mark J. Stefik
Mark J. Stefik Palo Alto Research Center
Jock D. Mackinlay
Jock D. Mackinlay Tableau Software (United States)
Greg Corrado
Greg Corrado Google (United States)
Michael S. Bernstein
Michael S. Bernstein Stanford University
Hanspeter Pfister
Hanspeter Pfister Harvard University

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