World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
44
Citations
45978
World Ranking
7335
National Ranking
65

Daniel J. 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 Daniel J. 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: 219 publications — 53rd percentile

53% of scientists in this discipline score the same or lower.

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

Daniel J. 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 Daniel J. 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: 44 D-Index — 48th percentile

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

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

Overview

Daniel J. Lee is affiliated with Samsung in South Korea and has contributed extensively to research spanning computer science and engineering fields. Their work integrates multiple disciplines, notably artificial intelligence, computer vision, control and systems engineering, cognitive neuroscience, and electrical and electronic engineering.

The research topics associated with Daniel J. Lee include:

  • Robot Manipulation and Learning
  • Robotic Path Planning Algorithms
  • Neural Networks and Applications
  • Spectroscopy and Laser Applications
  • Laser Design and Applications
  • Human Pose and Action Recognition
  • Neural dynamics and brain function

Recent publications by Daniel J. Lee demonstrate a breadth of expertise in both artificial intelligence and applied physics arenas. Among these are:

  • "Separability and geometry of object manifolds in deep neural networks" (2020), published in Nature Communications
  • "MHz laser absorption spectroscopy via diplexed RF modulation for pressure, temperature, and species in rotating detonation rocket flows" (2020), published in Applied Physics B
  • "Methane-oxygen rotating detonation exhaust thermodynamics with variable mixing, equivalence ratio, and mass flux" (2021), published in Aerospace Science and Technology
  • "Line mixing and broadening of carbon dioxide by argon in the v3 bandhead near 4.2 µm at high temperatures and high pressures" (2020), published in Journal of Quantitative Spectroscopy and Radiative Transfer
  • "Exploiting line-mixing effects for laser absorption spectroscopy at extreme combustion pressures" (2020), published in Proceedings of the Combustion Institute

The frequent co-authors in Daniel J. Lee's scholarly collaborations include:

  • Volkan Isler
  • Niko A. Grupen
  • Anil P. Nair
  • R. Mitchell Spearrin
  • Jinwook Huh

Daniel J. Lee has published significantly in venues such as arXiv (Cornell University), with 19 publications, as well as the Proceedings of the AAAI Conference on Artificial Intelligence and the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), each with multiple contributions. Other publication venues include Nature Communications and Applied Physics B.

The scientist's research addresses subjects at the intersection of artificial intelligence and engineering, leveraging methodologies and applications in neural networks, robotic systems, and spectroscopy. Their work reflects ongoing engagement with both fundamental and applied challenges in technology and science.

Best Publications

  • Learning the parts of objects by non-negative matrix factorization

    Daniel D. Lee;H. Sebastian Seung;H. Sebastian Seung

  • Stan: A Probabilistic Programming Language

    Bob Carpenter;Andrew Gelman;Matthew D. Hoffman;Daniel Lee

  • Algorithms for Non-negative Matrix Factorization

    Daniel D. Lee;H. Sebastian Seung

  • The manifold ways of perception

    H. Sebastian Seung;Daniel D. Lee

  • Grassmann discriminant analysis: a unifying view on subspace-based learning

    Jihun Hamm;Daniel D. Lee

  • A kernel view of the dimensionality reduction of manifolds

    Jihun Ham;Daniel D. Lee;Sebastian Mika;Bernhard Schölkopf

  • Stan: A Probabilistic Programming Language for Bayesian Inference and Optimization.

    Andrew Gelman;Daniel Lee;Jiqiang Guo

  • System and method for providing interactive dialogue and iterative search functions to find information

    Katherine G. August;Chin-Sheng Chuang;Michelle McNerney;Elizabeth A. Shriver

  • Stability of the memory of eye position in a recurrent network of conductance-based model neurons.

    H.Sebastian Seung;H.Sebastian Seung;Daniel D. Lee;Ben Y. Reis;David W. Tank

  • Multiplicative Updates for Nonnegative Quadratic Programming

    Fei Sha;Yuanqing Lin;Lawrence K. Saul;Daniel D. Lee

  • Spectral Methods for Dimensionality Reduction.

    Lawrence K. Saul;Kilian Q. Weinberger;Fei Sha;Jihun Ham

  • Multiplicative Updates for Nonnegative Quadratic Programming in Support Vector Machines

    Fei Sha;Lawrence K. Saul;Daniel D. Lee

  • Semisupervised alignment of manifolds.

    Jihun Ham;Daniel D. Lee;Lawrence K. Saul

  • Little Ben: The Ben Franklin Racing Team's entry in the 2007 DARPA Urban Challenge

    Jonathan Bohren;Tully Foote;Jim Keller;Alex Kushleyev

  • Learning Optimal Resource Allocations in Wireless Systems

    Mark Eisen;Clark Zhang;Luiz F. O. Chamon;Daniel D. Lee

  • Short-term memory in orthogonal neural networks.

    Olivia L. White;Daniel D. Lee;Haim Sompolinsky;Haim Sompolinsky

  • Approximating Explicit Model Predictive Control Using Constrained Neural Networks

    Steven Chen;Kelsey Saulnier;Nikolay Atanasov;Daniel D. Lee

  • Unsupervised Learning by Convex and Conic Coding

    Daniel D. Lee;H. Sebastian Seung

  • Separability and geometry of object manifolds in deep neural networks.

    Uri Cohen;SueYeon Chung;SueYeon Chung;SueYeon Chung;Daniel D. Lee;Haim Sompolinsky;Haim Sompolinsky

  • Generative Local Metric Learning for Nearest Neighbor Classification

    Yung-Kyun Noh;Byoung-Tak Zhang;Daniel D. Lee

  • The Rectified Gaussian Distribution

    Nicholas D. Socci;Daniel D. Lee;H. Sebastian Seung

  • Access Control for Home Data Sharing: Attitudes, Needs and Practices

    Michelle L. Mazurek;J. P. Arsenault;Joanna Bresee;Nitin Gupta

  • Access Control for Home Data Sharing: Attitudes, Needs and Practices (CMU-CyLab-09-013, CMU-PDL-09-110)

    Michelle L. Mazurek;J. P. Arsenault;Joanna Bresee;Nitin Gupta

Frequent Co-Authors

Volkan Isler
Volkan Isler University of Minnesota
H. Sebastian Seung
H. Sebastian Seung Princeton University
Byoung-Tak Zhang
Byoung-Tak Zhang Seoul National University
Haim Sompolinsky
Haim Sompolinsky Hebrew University of Jerusalem
Andrew Gelman
Andrew Gelman Columbia University
Lawrence K. Saul
Lawrence K. Saul University of California, San Diego
Vijay Kumar
Vijay Kumar University of Pennsylvania
George J. Pappas
George J. Pappas University of Pennsylvania
Frank C. Park
Frank C. Park Seoul National University
Fei Sha
Fei Sha Facebook (United States)

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