World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
6941
World Ranking
12444
National Ranking
5046

Eugene Wu 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 Eugene Wu 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: 116 publications — 13th percentile

13% of scientists in this discipline score the same or lower.

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

Eugene Wu 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 Eugene Wu 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: 33 D-Index — 13th percentile

13% of scientists in this discipline score the same or lower.

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

Overview

Eugene Wu is affiliated with Columbia University in the United States and is active in the field of Computer Science, with a publication record that spans multiple subfields including Artificial Intelligence, Computer Networks and Communications, Management Science and Operations Research, Computer Vision and Pattern Recognition, and Information Systems and Management.

Their research focuses on several main topics within data and computing, such as Data Quality and Management, Advanced Database Systems and Queries, Data Visualization and Analytics, Scientific Computing and Data Management, Topic Modeling, Privacy-Preserving Technologies in Data, and Data Management and Algorithms.

Eugene Wu has contributed to a substantial number of scientific papers published in well-known venues. Among the most frequent publication venues are arXiv (Cornell University), Proceedings of the VLDB Endowment, Proceedings of the 2022 International Conference on Management of Data, Proceedings of the ACM on Management of Data, and Proceedings of the National Academy of Sciences.

Their recent papers include these works:

  • A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level (2022), Proceedings of the National Academy of Sciences
  • PI2: End-to-end Interactive Visualization Interface Generation from Queries (2022), Proceedings of the 2022 International Conference on Management of Data
  • spade: Synthesizing Data Quality Assertions for Large Language Model Pipelines (2024), Proceedings of the VLDB Endowment
  • Enabling SQL-based training data debugging for federated learning (2021), Proceedings of the VLDB Endowment
  • Complaint-Driven Training Data Debugging at Interactive Speeds (2022), Proceedings of the 2022 International Conference on Management of Data

They have collaborated frequently with several co-authors, including:

  • Zezhou Huang
  • Jiannan Wang
  • Aditya Parameswaran
  • Yiru Chen
  • Weiyuan Wu

Best Publications

  • High-performance complex event processing over streams

    Eugene Wu;Yanlei Diao;Shariq Rizvi

  • WebTables: exploring the power of tables on the web

    Michael J. Cafarella;Alon Halevy;Daisy Zhe Wang;Eugene Wu

  • Relational Cloud: A Database-as-a-Service for the Cloud

    Carlo Curino;Evan Philip Charles Jones;Raluca Ada Popa;Nirmesh Malviya

  • Scorpion: explaining away outliers in aggregate queries

    Eugene Wu;Samuel Madden

  • Human-powered sorts and joins

    Adam Marcus;Eugene Wu;David Karger;Samuel Madden

  • Design Considerations for High Fan-In Systems: The HiFi Approach.

    Michael J. Franklin;Shawn R. Jeffery;Sailesh Krishnamurthy;Frederick Reiss

  • Crowdsourced Databases: Query Processing with People

    Adam Marcus;Eugene Wu;David R. Karger;Samuel R. Madden

  • TrajStore: An adaptive storage system for very large trajectory data sets

    Philippe Cudre-Mauroux;Eugene Wu;Samuel Madden

  • Relational Cloud: a Database Service for the cloud.

    Carlo Curino;Evan P. C. Jones;Raluca A. Popa;Nirmesh Malviya

  • ActiveClean: interactive data cleaning for statistical modeling

    Sanjay Krishnan;Jiannan Wang;Eugene Wu;Michael J. Franklin

  • Uncovering the Relational Web

    Michael J. Cafarella;Alon Y. Halevy;Yang Zhang;Daisy Zhe Wang

  • SASE: Complex Event Processing over Streams

    Daniel Gyllstrom;Eugene Wu;Hee-Jin Chae;Yanlei Diao

  • The case for data visualization management systems: vision paper

    Eugene Wu;Leilani Battle;Samuel R. Madden

  • Automated Metadata Construction to Support Portable Building Applications

    Arka A. Bhattacharya;Dezhi Hong;David Culler;Jorge Ortiz

  • PALM: Machine Learning Explanations For Iterative Debugging

    Sanjay Krishnan;Eugene Wu

  • Towards reliable interactive data cleaning: a user survey and recommendations

    Sanjay Krishnan;Daniel Haas;Michael J. Franklin;Eugene Wu

  • Collaborative data analytics with DataHub

    Anant Bhardwaj;Amol Deshpande;Aaron J. Elmore;David Karger

  • BoostClean: Automated Error Detection and Repair for Machine Learning

    Sanjay Krishnan;Michael J. Franklin;Ken Goldberg;Eugene Wu

  • CLAMShell: speeding up crowds for low-latency data labeling

    Daniel Haas;Jiannan Wang;Eugene Wu;Michael J. Franklin

  • ActiveClean: An Interactive Data Cleaning Framework For Modern Machine Learning

    Sanjay Krishnan;Michael J. Franklin;Ken Goldberg;Jiannan Wang

  • Smoke: fine-grained lineage at interactive speed

    Fotis Psallidas;Eugene Wu

Frequent Co-Authors

Michael J. Franklin
Michael J. Franklin University of Chicago
Remco Chang
Remco Chang Tufts University
Ken Goldberg
Ken Goldberg University of California, Berkeley
Alon Halevy
Alon Halevy Facebook (United States)
Philippe Cudré-Mauroux
Philippe Cudré-Mauroux University of Fribourg
Carlo Curino
Carlo Curino Microsoft (United States)

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