World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
6172
World Ranking
10712
National Ranking
122

Dongsu Han 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 Dongsu Han 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: 105 publications — 10th percentile

10% of scientists in this discipline score the same or lower.

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

Dongsu Han 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 Dongsu Han 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: 37 D-Index — 27th percentile

27% of scientists in this discipline score the same or lower.

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

Overview

Dongsu Han is affiliated with the Korea Advanced Institute of Science and Technology in South Korea and works primarily in the field of Computer Science. Their research spans multiple subfields including Computer Networks and Communications, Molecular Biology, Artificial Intelligence, Information Systems, and Signal Processing.

The scientist's work concentrates on several main topics:

  • Genomics and Phylogenetic Studies
  • Cloud Computing and Resource Management
  • Algorithms and Data Compression
  • Software-Defined Networks and 5G
  • Security and Verification in Computing
  • Advanced Malware Detection Techniques
  • Caching and Content Delivery

Throughout their career, Dongsu Han has published extensively, with notable papers including:

  • "BWA-MEME: BWA-MEM emulated with a machine learning approach," 2022, Bioinformatics
  • "Congestion Control for Cross-Datacenter Networks," 2022, IEEE/ACM Transactions on Networking
  • "BWA-MEME: BWA-MEM emulated with a machine learning approach," 2021, bioRxiv (Cold Spring Harbor Laboratory)
  • "A Secure Middlebox Framework for Enabling Visibility Over Multiple Encryption Protocols," 2020, IEEE/ACM Transactions on Networking
  • "Graph Neural Network-Based SLO-Aware Proactive Resource Autoscaling Framework for Microservices," 2024, IEEE/ACM Transactions on Networking

The scientist frequently collaborates with other researchers, including Youngmok Jung, Jinwoo Park, Byung-Kwon Choi, Juhyeng Han, and Seongmin Kim.

Dongsu Han's work is often published in well-known venues, primarily:

  • IEEE/ACM Transactions on Networking
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Bioinformatics
  • KIISE Transactions on Computing Practices
  • Crisis and Emergency Management Theory and Praxis

Best Publications

  • ATLAS: A scalable and high-performance scheduling algorithm for multiple memory controllers

    Yoongu Kim;Dongsu Han;Onur Mutlu;Mor Harchol-Balter

  • MICA: a holistic approach to fast in-memory key-value storage

    Hyeontaek Lim;Dongsu Han;David G. Andersen;Michael Kaminsky

  • mTCP: a highly scalable user-level TCP stack for multicore systems

    Eun Young Jeong;Shinae Woo;Muhammad Jamshed;Haewon Jeong

  • Information-agnostic flow scheduling for commodity data centers

    Wei Bai;Li Chen;Kai Chen;Dongsu Han

  • BWA-MEME: BWA-MEM emulated with a machine learning approach

    Unknown

  • Credit-Scheduled Delay-Bounded Congestion Control for Datacenters

    Inho Cho;Keon Jang;Dongsu Han

  • XIA: efficient support for evolvable internetworking

    Dongsu Han;Ashok Anand;Fahad Dogar;Boyan Li

  • SGX-Shield: Enabling Address Space Layout Randomization for SGX Programs.

    Jaebaek Seo;Byoungyoung Lee;Seong Min Kim;Ming-Wei Shih

  • SoftNIC: A Software NIC to Augment Hardware

    Sangjin Han;Keon Jang;Aurojit Panda;Shoumik Palkar

  • Guaranteeing Deadlines for Inter-Data Center Transfers

    Hong Zhang;Kai Chen;Wei Bai;Dongsu Han

  • XIA: an architecture for an evolvable and trustworthy internet

    Ashok Anand;Fahad Dogar;Dongsu Han;Boyan Li

  • Guaranteeing deadlines for inter-datacenter transfers

    Hong Zhang;Kai Chen;Wei Bai;Dongsu Han

  • Access Point Localization Using Local Signal Strength Gradient

    Dongsu Han;David G. Andersen;Michael Kaminsky;Konstantina Papagiannaki

  • Practical, Real-time Centralized Control for CDN-based Live Video Delivery

    Matthew K. Mukerjee;David Naylor;Junchen Jiang;Dongsu Han

  • OpenSGX: An Open Platform for SGX Research

    Prerit Jain;Soham Desai;Seong Min Kim;Ming Wei Shih

  • Neural-Enhanced Live Streaming: Improving Live Video Ingest via Online Learning

    Jaehong Kim;Youngmok Jung;Hyunho Yeo;Juncheol Ye

  • Neural adaptive content-aware internet video delivery

    Hyunho Yeo;Youngmok Jung;Jaehong Kim;Jinwoo Shin

  • NEMO: enabling neural-enhanced video streaming on commodity mobile devices

    Hyunho Yeo;Chan Ju Chong;Youngmok Jung;Juncheol Ye

  • Accurate latency-based congestion feedback for datacenters

    Changhyun Lee;Chunjong Park;Keon Jang;Sue Moon

  • PIAS: Practical Information-Agnostic Flow Scheduling for Commodity Data Centers

    Wei Bai;Li Chen;Kai Chen;Dongsu Han

  • Cybercriminal Minds: An investigative study of cryptocurrency abuses in the Dark Web.

    Seunghyeon Lee;Changhoon Yoon;Heedo Kang;Yeonkeun Kim

  • A First Step Towards Leveraging Commodity Trusted Execution Environments for Network Applications

    Seongmin Kim;Youjung Shin;Jaehyung Ha;Taesoo Kim

  • BWA-MEME: BWA-MEM emulated with a machine learning approach

    Youngmok Jung;Dongsu Han

Frequent Co-Authors

Srinivasan Seshan
Srinivasan Seshan Carnegie Mellon University
Wei Bai
Wei Bai Microsoft (United States)
David G. Andersen
David G. Andersen Carnegie Mellon University
Kai Chen
Kai Chen Hong Kong University of Science and Technology
Taesoo Kim
Taesoo Kim Georgia Institute of Technology
Michael Kaminsky
Michael Kaminsky Carnegie Mellon University
Hong Xu
Hong Xu City University of Hong Kong
Aditya Akella
Aditya Akella The University of Texas at Austin
Yongdae Kim
Yongdae Kim Korea Advanced Institute of Science and Technology
Hao Wang
Hao Wang Tianjin University

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