World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
43
Citations
8506
World Ranking
7939
National Ranking
3422

Chang-Tien 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 Chang-Tien 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: 232 publications — 57th percentile

57% of scientists in this discipline score the same or lower.

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

Chang-Tien 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 Chang-Tien 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: 43 D-Index — 46th percentile

46% of scientists in this discipline score the same or lower.

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

Overview

Chang-Tien Lu is affiliated with Virginia Tech in the United States and works primarily in the field of Computer Science, with a focus on Artificial Intelligence. Their research spans several subfields, including Computer Vision and Pattern Recognition, Building and Construction, Statistical and Nonlinear Physics, and Transportation.

The scientist's work covers a range of main topics, particularly in areas related to data analysis and machine learning methodologies. Key topics include:

  • Traffic Prediction and Management Techniques
  • Topic Modeling
  • Advanced Graph Neural Networks
  • Complex Network Analysis Techniques
  • Human Mobility and Location-Based Analysis
  • Anomaly Detection Techniques and Applications
  • Domain Adaptation and Few-Shot Learning

Chang-Tien Lu has contributed to numerous publications, including journal articles and conference papers. Notable recent papers include:

  • "TapNet: Multivariate Time Series Classification with Attentional Prototypical Network," 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • "Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks," 2020, arXiv (Cornell University)
  • "Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks," 2023, ACM Computing Surveys
  • "Probabilistic Topic Modeling for Comparative Analysis of Document Collections," 2020, ACM Transactions on Knowledge Discovery from Data
  • "The Quality of AI-Generated Dental Caries Multiple Choice Questions: A Comparative Analysis of ChatGPT and Google Bard Language Models," 2024, Heliyon

The scientist frequently publishes in the following venues:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • 2022 IEEE International Conference on Big Data (Big Data)
  • ACM Transactions on Knowledge Discovery from Data
  • Frontiers in Big Data

Chang-Tien Lu has collaborated extensively with a number of coauthors, including:

  • Fanglan Chen
  • Zhiqian Chen
  • Kaiqun Fu
  • Jianfeng He
  • Shuo Lei

Best Publications

  • Advances in Spatial and Temporal Databases

    Michael Gertz;Matthias Renz;Xiaofang Zhou;Erik Hoel

  • Survey of fraud detection techniques

    Yufeng Kou;Chang-Tien Lu;S. Sirwongwattana;Yo-Ping Huang

  • Spatial databases-accomplishments and research needs

    S. Shekhar;S. Chawla;S. Ravada;A. Fetterer

  • A Unified Approach to Detecting Spatial Outliers

    Shashi Shekhar;Chang-Tien Lu;Pusheng Zhang

  • 'Beating the news' with EMBERS: forecasting civil unrest using open source indicators

    Naren Ramakrishnan;Patrick Butler;Sathappan Muthiah;Nathan Self

  • TapNet: Multivariate Time Series Classification with Attentional Prototypical Network

    Xuchao Zhang;Yifeng Gao;Jessica Lin;Chang-Tien Lu

  • Detecting graph-based spatial outliers: algorithms and applications (a summary of results)

    Shashi Shekhar;Chang-Tien Lu;Pusheng Zhang

  • Algorithms for spatial outlier detection

    C.-T. Lu;D. Chen;Y. Kou

  • Misinformation Propagation in the Age of Twitter

    Fang Jin;Wei Wang;Liang Zhao;Edward Dougherty

  • Spatial Weighted Outlier Detection.

    Yufeng Kou;Chang-Tien Lu;Dechang Chen

  • Multi-Task Learning for Spatio-Temporal Event Forecasting

    Liang Zhao;Qian Sun;Jieping Ye;Feng Chen

  • Crowdsourcing Cybersecurity: Cyber Attack Detection using Social Media

    Rupinder Paul Khandpur;Taoran Ji;Steve Jan;Gang Wang

  • CROWDSAFE: crowd sourcing of crime incidents and safe routing on mobile devices

    Sumit Shah;Fenye Bao;Chang-Tien Lu;Ing-Ray Chen

  • Semi-Supervised Deep Learning Approach for Transportation Mode Identification Using GPS Trajectory Data

    Sina Dabiri;Chang-Tien Lu;Kevin Heaslip;Chandan K. Reddy

  • On Detecting Spatial Outliers

    Dechang Chen;Chang-Tien Lu;Yufeng Kou;Feng Chen

  • Map cube: A visualization tool for spatial data warehouses

    S Shekhar;C T Lu;X Tan;S Chawla

  • Detecting spatial outliers with multiple attributes

    Chang-Tien Lu;Dechang Chen;Yufeng Kou

  • Activity analysis based on low sample rate smart meters

    Feng Chen;Jing Dai;Bingsheng Wang;Sambit Sahu

  • SOSNet: A Graph Convolutional Network Approach to Fine-Grained Cyberbullying Detection

    Jason Wang;Kaiqun Fu;Chang-Tien Lu

  • Detecting graph-based spatial outliers

    Shashi Shekhar;Chang-Tien Lu;Pusheng Zhang

  • Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

    Ouri Wolfson;Divyakant Agrawal;Chang-Tien Lu

  • Unsupervised Spatial Event Detection in Targeted Domains with Applications to Civil Unrest Modeling

    Liang Zhao;Feng Chen;Jing Dai;Ting Hua

  • SimNest: Social Media Nested Epidemic Simulation via Online Semi-Supervised Deep Learning

    Liang Zhao;Jiangzhuo Chen;Feng Chen;Wei Wang

Frequent Co-Authors

Naren Ramakrishnan
Naren Ramakrishnan Virginia Tech
Shashi Shekhar
Shashi Shekhar University of Minnesota
Sanjay Chawla
Sanjay Chawla Qatar Computing Research Institute
Lei Zhang
Lei Zhang Hong Kong Polytechnic University
Yanjie Fu
Yanjie Fu Arizona State University
Ing-Ray Chen
Ing-Ray Chen Virginia Tech
Aravind Srinivasan
Aravind Srinivasan University of Maryland, College Park
Chandan K. Reddy
Chandan K. Reddy Virginia Tech
Lise Getoor
Lise Getoor University of California, Santa Cruz
Yan Huang
Yan Huang University of North Texas

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