World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
45
Citations
10922
World Ranking
7087
National Ranking
3112

Shan Lu 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 Shan Lu 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: 133 publications — 20th percentile

20% of scientists in this discipline score the same or lower.

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

Shan Lu 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 Shan Lu 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: 45 D-Index — 51st percentile

51% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2019 - ACM Distinguished Member
  • 2014 - Fellow of Alfred P. Sloan Foundation

Overview

Shan Lu is affiliated with the University of Chicago in the United States. Their research spans multiple fields with a primary focus on Computer Science and Biochemistry, Genetics and Molecular Biology.

In Computer Science, their work covers specialized areas such as Artificial Intelligence, Computer Networks and Communications, and Information Systems. In the biological sciences, Shan Lu's contributions are mainly in Molecular Biology and Cancer Research.

The scientist's research topics include:

  • Single-cell and spatial transcriptomics
  • Gene Regulatory Network Analysis
  • Adversarial Robustness in Machine Learning
  • Software System Performance and Reliability
  • Distributed systems and fault tolerance
  • Cloud Computing and Resource Management
  • Software Engineering Research

Shan Lu's recent published papers illustrate this multidisciplinary approach. Some of the notable publications are:

  • "Trace2TAP" (2020) in Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies
  • "Automated testing of software that uses machine learning APIs" (2022) in Proceedings of the 44th International Conference on Software Engineering
  • "Debiased personalized gene coexpression networks for population-scale scRNA-seq data" (2023) in Genome Research
  • "An integrated analysis of single-cell and bulk transcriptomics reveals EFNA1 as a novel prognostic biomarker for cervical cancer" (2022) in Human Cell
  • "Secondary bile acids function through the vitamin D receptor in myeloid progenitors to promote myelopoiesis" (2023) in Blood Advances

Frequent collaborators include:

  • Sündüz Keleş
  • Chengcheng Wan
  • Sophie Xie
  • Henry Hoffmann
  • Michael Maire

Shan Lu publishes regularly in various venues. Their most common outlets are:

  • arXiv (Cornell University)
  • ACM Transactions on Software Engineering and Methodology
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies
  • Proceedings of the ACM on Programming Languages

Recognition includes honors such as being named an ACM Distinguished Member in 2019 and a Fellow of the Alfred P. Sloan Foundation in 2014.

Best Publications

  • Learning from mistakes: a comprehensive study on real world concurrency bug characteristics

    Shan Lu;Soyeon Park;Eunsoo Seo;Yuanyuan Zhou

  • CP-Miner: finding copy-paste and related bugs in large-scale software code

    Z. Li;S. Lu;S. Myagmar;Y. Zhou

  • AVIO: detecting atomicity violations via access interleaving invariants

    Shan Lu;Joseph Tucek;Feng Qin;Yuanyuan Zhou

  • CP-Miner: a tool for finding copy-paste and related bugs in operating system code

    Zhenmin Li;Shan Lu;Suvda Myagmar;Yuanyuan Zhou

  • Understanding and detecting real-world performance bugs

    Guoliang Jin;Linhai Song;Xiaoming Shi;Joel Scherpelz

  • CTrigger: exposing atomicity violation bugs from their hiding places

    Soyeon Park;Shan Lu;Yuanyuan Zhou

  • Have things changed now?: an empirical study of bug characteristics in modern open source software

    Zhenmin Li;Lin Tan;Xuanhui Wang;Shan Lu

  • PRES: probabilistic replay with execution sketching on multiprocessors

    Soyeon Park;Yuanyuan Zhou;Weiwei Xiong;Zuoning Yin

  • MUVI: automatically inferring multi-variable access correlations and detecting related semantic and concurrency bugs

    Shan Lu;Soyeon Park;Chongfeng Hu;Xiao Ma

  • Automated atomicity-violation fixing

    Guoliang Jin;Linhai Song;Wei Zhang;Shan Lu

  • A Study of Linux File System Evolution

    Lanyue Lu;Andrea C. Arpaci-Dusseau;Remzi H. Arpaci-Dusseau;Shan Lu

  • SafeMem: exploiting ECC-memory for detecting memory leaks and memory corruption during production runs

    Feng Qin;Shan Lu;Yuanyuan Zhou

  • Automated concurrency-bug fixing

    Guoliang Jin;Wei Zhang;Dongdong Deng;Ben Liblit

  • Triage: diagnosing production run failures at the user's site

    Joseph Tucek;Shan Lu;Chengdu Huang;Spiros Xanthos

  • Toddler: detecting performance problems via similar memory-access patterns

    Adrian Nistor;Linhai Song;Darko Marinov;Shan Lu

  • AccMon: Automatically Detecting Memory-Related Bugs via Program Counter-Based Invariants

    Pin Zhou;Wei Liu;Long Fei;Shan Lu

  • ConSeq: detecting concurrency bugs through sequential errors

    Wei Zhang;Junghee Lim;Ramya Olichandran;Joel Scherpelz

  • TaxDC: A Taxonomy of Non-Deterministic Concurrency Bugs in Datacenter Distributed Systems

    Tanakorn Leesatapornwongsa;Jeffrey F. Lukman;Shan Lu;Haryadi S. Gunawi

  • ConMem: detecting severe concurrency bugs through an effect-oriented approach

    Wei Zhang;Chong Sun;Shan Lu

  • Instrumentation and sampling strategies for cooperative concurrency bug isolation

    Guoliang Jin;Aditya Thakur;Ben Liblit;Shan Lu

Frequent Co-Authors

Yuanyuan Zhou
Yuanyuan Zhou University of California, San Diego
Henry Hoffmann
Henry Hoffmann University of Chicago
Michael Maire
Michael Maire University of Chicago
Xiaohui Gu
Xiaohui Gu North Carolina State University
Alvin Cheung
Alvin Cheung University of California, Berkeley
Ben Liblit
Ben Liblit University of Wisconsin–Madison
Blase Ur
Blase Ur University of Chicago
Josep Torrellas
Josep Torrellas University of Illinois at Urbana-Champaign
Weimin Zheng
Weimin Zheng Tsinghua University
Karthikeyan Sankaralingam
Karthikeyan Sankaralingam University of Wisconsin–Madison

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