World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
41
Citations
7938
World Ranking
8767
National Ranking
88

Jinwoo Shin 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 Jinwoo Shin 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: 245 publications — 61st percentile

61% of scientists in this discipline score the same or lower.

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

Jinwoo Shin 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 Jinwoo Shin 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

Jinwoo Shin is affiliated with the Korea Advanced Institute of Science and Technology in South Korea, contributing extensively to the field of computer science with a focus on artificial intelligence and computer vision.

The primary domains of Shin's research include:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Applied Mathematics
  • Computational Theory and Mathematics
  • Computer Graphics and Computer-Aided Design

The main research topics covered in their work are:

  • Domain Adaptation and Few-Shot Learning
  • Reinforcement Learning in Robotics
  • Advanced Neural Network Applications
  • Adversarial Robustness in Machine Learning
  • Multimodal Machine Learning Applications
  • Generative Adversarial Networks and Image Synthesis
  • Anomaly Detection Techniques and Applications

Shin has published a significant number of papers, predominantly on the arXiv platform, totaling 129 publications there. Other notable venues include:

  • Proceedings of the AAAI Conference on Artificial Intelligence
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • SSRN Electronic Journal
  • IEEE Access

Recent selected publications illustrate the scope of their research:

  • CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances, 2020, arXiv (Cornell University)
  • Co2L: Contrastive Continual Learning, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs, 2020, arXiv (Cornell University)
  • Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning, 2020, arXiv (Cornell University)
  • Layer-adaptive sparsity for the Magnitude-based Pruning, 2020, arXiv (Cornell University)

Frequent collaborators contributing to these research efforts include:

  • Jongheon Jeong
  • Kimin Lee
  • Younggyo Seo
  • Jihoon Tack
  • Jaeho Lee

Best Publications

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

    Kimin Lee;Kibok Lee;Honglak Lee;Jinwoo Shin

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

    Kimin Lee;Honglak Lee;Kibok Lee;Jinwoo Shin

  • Co2L: Contrastive Continual Learning

    Unknown

  • Regularizing Class-Wise Predictions via Self-Knowledge Distillation

    Sukmin Yun;Jongjin Park;Kimin Lee;Jinwoo Shin

  • Network adiabatic theorem: an efficient randomized protocol for contention resolution

    Shreevatsa Rajagopalan;Devavrat Shah;Jinwoo Shin

  • CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances

    Jihoon Tack;Sangwoo Mo;Jongheon Jeong;Jinwoo Shin

  • M2m: Imbalanced Classification via Major-to-Minor Translation

    Jaehyung Kim;Jongheon Jeong;Jinwoo Shin

  • Overcoming Catastrophic Forgetting With Unlabeled Data in the Wild

    Kibok Lee;Kimin Lee;Jinwoo Shin;Honglak Lee

  • Distributed Random Access Algorithm: Scheduling and Congestion Control

    Libin Jiang;D Shah;Jinwoo Shin;J Walrand

  • Video Probabilistic Diffusion Models in Projected Latent Space

    Unknown

  • InstaGAN: Instance-aware Image-to-Image Translation

    Sangwoo Mo;Minsu Cho;Jinwoo Shin

  • Randomized Scheduling Algorithm for Queueing Networks

    Devavrat Shah;Jinwoo Shin

  • Freeze Discriminator: A Simple Baseline for Fine-tuning GANs.

    Sangwoo Mo;Minsu Cho;Jinwoo Shin

  • Neural adaptive content-aware internet video delivery

    Hyunho Yeo;Youngmok Jung;Jaehong Kim;Jinwoo Shin

  • MetaSense: few-shot adaptation to untrained conditions in deep mobile sensing

    Taesik Gong;Yeonsu Kim;Jinwoo Shin;Sung-Ju Lee

  • Learning from Failure: De-biasing Classifier from Biased Classifier

    Jun Hyun Nam;Hyuntak Cha;Sungsoo Ahn;Jaeho Lee

  • Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning

    Kimin Lee;Kibok Lee;Jinwoo Shin;Honglak Lee

  • Learning What and Where to Transfer

    Yunhun Jang;Hankook Lee;Sung Ju Hwang;Jinwoo Shin

  • Approximating Spectral Sums of Large-Scale Matrices using Stochastic Chebyshev Approximations

    Insu Han;Dmitry Malioutov;Haim Avron;Jinwoo Shin

  • Large-scale log-determinant computation through stochastic Chebyshev expansions

    Insu Han;Dmitry Malioutov;Jinwoo Shin

  • Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning

    Jaehyung Kim;Youngbum Hur;Sejun Park;Eunho Yang

  • Learning from Failure: Training Debiased Classifier from Biased Classifier.

    Jun Hyun Nam;Hyuntak Cha;Sungsoo Ahn;Jaeho Lee

  • Consistency Regularization for Certified Robustness of Smoothed Classifiers

    Jongheon Jeong;Jinwoo Shin

Frequent Co-Authors

Yung Yi
Yung Yi Korea Advanced Institute of Science and Technology
Sung Ju Hwang
Sung Ju Hwang Korea Advanced Institute of Science and Technology
Honglak Lee
Honglak Lee University of Michigan–Ann Arbor
Michael Chertkov
Michael Chertkov University of Arizona
Sewoong Oh
Sewoong Oh University of Washington
Pieter Abbeel
Pieter Abbeel University of California, Berkeley
Kihyuk Sohn
Kihyuk Sohn Google (United States)
Song Chong
Song Chong Korea Advanced Institute of Science and Technology
Sung-Ju Lee
Sung-Ju Lee Korea Advanced Institute of Science and Technology

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