World's Best Scientists 2026 revealed!
Heung-Il Suk

Heung-Il Suk

D-Index & Metrics

Computer Science

D-Index
42
Citations
13143
World Ranking
8172
National Ranking
81

Heung-Il Suk 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 Heung-Il Suk 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: 187 publications — 41st percentile

41% of scientists in this discipline score the same or lower.

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

Heung-Il Suk 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 Heung-Il Suk 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: 42 D-Index — 43rd percentile

43% of scientists in this discipline score the same or lower.

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

Overview

Heung-Il Suk is affiliated with Korea University in South Korea and has a research profile encompassing neuroscience, computer science, and medicine. Their work spans multiple subfields such as cognitive neuroscience, artificial intelligence, radiology, nuclear medicine and imaging, computer vision and pattern recognition, and neurology. The scientist's main research topics include machine learning applications in healthcare, functional brain connectivity studies, EEG and brain-computer interfaces, neural dynamics and brain function, brain tumor detection and classification, radiomics and machine learning in medical imaging, and the use of AI in cancer detection.

The scientist has published frequently in a variety of venues including:

  • arXiv (Cornell University)
  • UNC Libraries
  • IEEE Transactions on Neural Networks and Learning Systems
  • NeuroImage
  • Korean Journal of Radiology

Frequent co-authors collaborating with Heung-Il Suk are:

  • Eunjin Jeon
  • Jee Seok Yoon
  • Wonjun Ko
  • Wonsik Jung
  • Eunsong Kang

Selected recent papers authored or co-authored by Heung-Il Suk include:

  • Multi-Scale Neural Network for EEG Representation Learning in BCI, 2021, IEEE Computational Intelligence Magazine
  • Deep Learning Algorithm for Automated Segmentation and Volume Measurement of the Liver and Spleen Using Portal Venous Phase Computed Tomography Images, 2020, Korean Journal of Radiology
  • TransSleep: Transitioning-Aware Attention-Based Deep Neural Network for Sleep Staging, 2022, IEEE Transactions on Cybernetics
  • A Survey on Deep Learning-Based Short/Zero-Calibration Approaches for EEG-Based Brain-Computer Interfaces, 2021, Frontiers in Human Neuroscience
  • Identifying resting-state effective connectivity abnormalities in drug-naïve major depressive disorder diagnosis via graph convolutional networks, 2020, Human Brain Mapping

Best Publications

  • Deep Learning in Medical Image Analysis

    Dinggang Shen;Guorong Wu;Heung Il Suk

  • Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis.

    Heung-Il Suk;Seong-Whan Lee;Dinggang Shen

  • Latent feature representation with stacked auto-encoder for AD/MCI diagnosis

    Heung Il Suk;Seong Whan Lee;Dinggang Shen;Dinggang Shen

  • Deep learning based imaging data completion for improved brain disease diagnosis.

    Rongjian Li;Wenlu Zhang;Heung Il Suk;Li Wang

  • Deep Learning-Based Feature Representation for AD/MCI Classification

    Heung Il Suk;Dinggang Shen

  • State-space model with deep learning for functional dynamics estimation in resting-state fMRI

    Heung Il Suk;Chong Yaw Wee;Seong Whan Lee;Dinggang Shen

  • Deep ensemble learning of sparse regression models for brain disease diagnosis

    Heung Il Suk;Seong Whan Lee;Dinggang Shen;Dinggang Shen

  • A Novel Bayesian Framework for Discriminative Feature Extraction in Brain-Computer Interfaces

    Heung-Il Suk;Seong-Whan Lee

  • A novel relational regularization feature selection method for joint regression and classification in AD diagnosis

    Xiaofeng Zhu;Heung-Il Suk;Li Wang;Seong-Whan Lee

  • Hand gesture recognition based on dynamic Bayesian network framework

    Heung-Il Suk;Bong-Kee Sin;Seong-Whan Lee

  • A novel matrix-similarity based loss function for joint regression and classification in AD diagnosis

    Xiaofeng Zhu;Heung Il Suk;Dinggang Shen;Dinggang Shen

  • Subspace Regularized Sparse Multitask Learning for Multiclass Neurodegenerative Disease Identification

    Xiaofeng Zhu;Heung-Il Suk;Seong-Whan Lee;Dinggang Shen

  • Deep sparse multi-task learning for feature selection in Alzheimer's disease diagnosis.

    Heung Il Suk;Seong Whan Lee;Dinggang Shen;Dinggang Shen

  • Commanding a Brain-Controlled Wheelchair Using Steady-State Somatosensory Evoked Potentials

    Keun-Tae Kim;Heung-Il Suk;Seong-Whan Lee

  • Person authentication from neural activity of face-specific visual self-representation

    Seul-Ki Yeom;Heung-Il Suk;Seong-Whan Lee

  • Non-homogeneous spatial filter optimization for ElectroEncephaloGram (EEG)-based motor imagery classification

    Tae-Eui Kam;Heung-Il Suk;Seong-Whan Lee

  • Multimodal manifold-regularized transfer learning for MCI conversion prediction

    Bo Cheng;Bo Cheng;Bo Cheng;Mingxia Liu;Heung Il Suk;Dinggang Shen;Dinggang Shen

  • Canonical feature selection for joint regression and multi-class identification in Alzheimer’s disease diagnosis

    Xiaofeng Zhu;Heung Il Suk;Seong Whan Lee;Dinggang Shen;Dinggang Shen

  • Subject and class specific frequency bands selection for multiclass motor imagery classification

    Heung-Il Suk;Seong-Whan Lee

  • Multi-Scale Neural Network for EEG Representation Learning in BCI

    Wonjun Ko;Eunjin Jeon;Seungwoo Jeong;Heung-Il Suk

  • Predicting BCI Subject Performance Using Probabilistic Spatio-Temporal Filters

    Heung Il Suk;Siamac Fazli;Jan Mehnert;Klaus Robert Müller

Frequent Co-Authors

Dinggang Shen
Dinggang Shen ShanghaiTech University
Seong-Whan Lee
Seong-Whan Lee Korea University
Chong Yaw Wee
Chong Yaw Wee University of North Carolina at Chapel Hill
Yinghuan Shi
Yinghuan Shi Nanjing University
Guorong Wu
Guorong Wu University of North Carolina at Chapel Hill
Yang Gao
Yang Gao Google (United Kingdom)
Heng Huang
Heng Huang University of Pittsburgh
Daoqiang Zhang
Daoqiang Zhang Nanjing University of Aeronautics and Astronautics
Klaus-Robert Müller
Klaus-Robert Müller Technical University of Berlin
Robert T. Knight
Robert T. Knight University of California, Berkeley

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