World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
91
Citations
52175
World Ranking
561
National Ranking
299

Honglak Lee 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 Honglak Lee 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: 204 publications — 48th percentile

48% of scientists in this discipline score the same or lower.

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

Honglak Lee 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 Honglak Lee 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: 91 D-Index — 96th percentile

96% of scientists in this discipline score the same or lower.

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

Overview

Honglak Lee is affiliated with the University of Michigan-Ann Arbor in the United States and has contributed extensively to the field of computer science, with a particular focus on artificial intelligence and its applications.

The research work by Honglak Lee spans multiple main topics, including:

  • Multimodal Machine Learning Applications
  • Domain Adaptation and Few-Shot Learning
  • Reinforcement Learning in Robotics
  • Natural Language Processing Techniques
  • Topic Modeling
  • Cell Image Analysis Techniques
  • Generative Adversarial Networks and Image Synthesis

Lee's primary fields of study encompass computer science with subfields in:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Biophysics
  • Radiology, Nuclear Medicine and Imaging
  • Statistical and Nonlinear Physics

The frequent collaborators who have co-authored works with Lee include:

  • Sungryull Sohn
  • Lajanugen Logeswaran
  • Todd Hollon
  • Akhil Kondepudi
  • Moontae Lee

Lee has published in a variety of reputable venues. Among the frequent publication platforms are:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Neuro-Oncology
  • Nature Medicine
  • Scientific Reports

Representative recent papers authored or co-authored by Lee include:

  • Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks, 2020, Nature Medicine
  • Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging, 2023, Nature Medicine
  • Improved Consistency Regularization for GANs, 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • Enriched CNN-Transformer Feature Aggregation Networks for Super-Resolution, 2023, 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
  • Pure Transformers are Powerful Graph Learners, 2022, arXiv (Cornell University)

Best Publications

  • Efficient sparse coding algorithms

    Honglak Lee;Alexis Battle;Rajat Raina;Andrew Y. Ng

  • Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations

    Honglak Lee;Roger Grosse;Rajesh Ranganath;Andrew Y. Ng

  • An analysis of single-layer networks in unsupervised feature learning

    Adam Coates;Andrew Y. Ng;Honglak Lee

  • Multimodal Deep Learning

    Jiquan Ngiam;Aditya Khosla;Mingyu Kim;Juhan Nam

  • Generative adversarial text to image synthesis

    Scott Reed;Zeynep Akata;Xinchen Yan;Lajanugen Logeswaran

  • Learning structured output representation using deep conditional generative models

    Kihyuk Sohn;Xinchen Yan;Honglak Lee

  • Self-taught learning: transfer learning from unlabeled data

    Rajat Raina;Alexis Battle;Honglak Lee;Benjamin Packer

  • Deep learning for detecting robotic grasps

    Ian Lenz;Honglak Lee;Ashutosh Saxena

  • Unsupervised feature learning for audio classification using convolutional deep belief networks

    Honglak Lee;Peter Pham;Yan Largman;Andrew Y. Ng

  • Sparse deep belief net model for visual area V2

    Honglak Lee;Chaitanya Ekanadham;Andrew Y. Ng

  • Evaluation of output embeddings for fine-grained image classification

    Zeynep Akata;Scott Reed;Daniel Walter;Honglak Lee

  • A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks

    Kimin Lee;Kibok Lee;Honglak Lee;Jinwoo Shin

  • Learning Deep Representations of Fine-Grained Visual Descriptions

    Scott Reed;Zeynep Akata;Honglak Lee;Bernt Schiele

  • Attribute2Image: Conditional Image Generation from Visual Attributes

    Xinchen Yan;Jimei Yang;Kihyuk Sohn;Honglak Lee

  • TRAINING DEEP NEURAL NETWORKS ON NOISY LABELS WITH BOOTSTRAPPING

    Scott E. Reed;Honglak Lee;Dragomir Anguelov;Christian Szegedy

  • Action-conditional video prediction using deep networks in Atari games

    Junhyuk Oh;Xiaoxiao Guo;Honglak Lee;Richard Lewis

  • Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks

    Todd C. Hollon;Balaji Pandian;Arjun R. Adapa;Esteban Urias

  • Learning Latent Dynamics for Planning from Pixels

    Danijar Hafner;Timothy P. Lillicrap;Ian Fischer;Ruben Villegas

  • Data-Efficient Hierarchical Reinforcement Learning

    Ofir Nachum;Shixiang Gu;Honglak Lee;Sergey Levine

  • Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples

    Kimin Lee;Honglak Lee;Kibok Lee;Jinwoo Shin

  • Learning Deep Representations of Fine-grained Visual Descriptions

    Scott Reed;Zeynep Akata;Bernt Schiele;Honglak Lee

  • Zero-Shot Learning with Structured Embeddings

    Zeynep Akata;Honglak Lee;Bernt Schiele

Frequent Co-Authors

Jinwoo Shin
Jinwoo Shin Korea Advanced Institute of Science and Technology
Kihyuk Sohn
Kihyuk Sohn Google (United States)
Satinder Singh
Satinder Singh DeepMind (United Kingdom)
Andrew Y. Ng
Andrew Y. Ng Stanford University
Jimei Yang
Jimei Yang Adobe Systems (United States)
Dumitru Erhan
Dumitru Erhan Google (United States)
Bernt Schiele
Bernt Schiele Max Planck Institute for Informatics
Richard L. Lewis
Richard L. Lewis University of Michigan–Ann Arbor
Zeynep Akata
Zeynep Akata University of Tübingen
Dragomir R. Radev
Dragomir R. Radev Yale University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

With the demand for tech professionals surging, many students are exploring flexible online learning options to fast-track their careers. If you're interested in entering the tech field quickly, there are several quick certifications that pay well. These certifications often focus on practical skills in areas like IT support, cybersecurity, or programming, and can lead to entry-level roles without a traditional degree.

For those aiming for advanced positions or higher salaries, pursuing a master’s is a smart move. Many universities now offer the shortest masters degree programs online, allowing you to earn a recognized qualification in as little as a year. These accelerated programs offer a flexible study schedule for working professionals.

If you want to maximize your investment, consider one of the most in demand masters degrees. Degrees focusing on computer science, data science, and AI are highly valued by employers and frequently lead to lucrative positions.

Those looking for a shorter online commitment can explore 1 year associate degree programs online. These programs help you build a foundation in technology and can serve as a stepping stone toward future bachelor’s or master’s studies.

Best Scientists Citing Honglak Lee

Trending Scientists