World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
56
Citations
10756
World Ranking
2857
National Ranking
869

Jianjun Shi publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Jianjun Shi sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 134 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 117 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 59 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 192 publications — 45th percentile

45% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Jianjun Shi D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Jianjun Shi sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 128 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 349 scientists 41 D-Index: 362 scientists 42 D-Index: 425 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 94 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 24 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 56 D-Index — 72nd percentile

72% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Jianjun Shi is affiliated with the Georgia Institute of Technology in the United States. Their research primarily spans the fields of Engineering and Computer Science, with significant contributions to Industrial and Manufacturing Engineering, Control and Systems Engineering, and Computer Vision and Pattern Recognition among other subfields.

Their work covers diverse topics including:

  • Industrial Vision Systems and Defect Detection
  • Manufacturing Process and Optimization
  • Advanced Statistical Process Monitoring
  • Tensor Decomposition and Applications
  • Fault Detection and Control Systems
  • Sparse and Compressive Sensing Techniques
  • Control Systems and Identification

Jianjun Shi has published extensively in several venues. The most frequent publication outlets include:

  • IISE Transactions
  • arXiv (Cornell University)
  • IEEE Transactions on Automation Science and Engineering
  • Journal of Manufacturing Science and Engineering
  • Technometrics

Among recent papers authored or co-authored by Jianjun Shi and colleagues are:

  • "A Deep Learning Based Data Fusion Method for Degradation Modeling and Prognostics," 2020, IEEE Transactions on Reliability
  • "Active Learning for Gaussian Process Considering Uncertainties With Application to Shape Control of Composite Fuselage," 2020, IEEE Transactions on Automation Science and Engineering
  • "A Lightweight One-Stage Defect Detection Network for Small Object Based on Dual Attention Mechanism and PAFPN," 2021, Frontiers in Physics
  • "In-process quality improvement: Concepts, methodologies, and applications," 2022, IISE Transactions
  • "Dynamic Multivariate Functional Data Modeling via Sparse Subspace Learning," 2020, Technometrics

The scientist has collaborated frequently with colleagues including Shancong Mou, Michael Biehler, Andi Wang, Zhen Zhong, and Jeffrey H. Hunt. These collaborations have resulted in multiple co-authored publications, reflecting ongoing research partnerships.

Best Publications

  • State Space Modeling of Sheet Metal Assembly for Dimensional Control

    Jionghua Jin;Jianjun Shi

  • Active Balancing and Vibration Control of Rotating Machinery: A Survey

    Shiyu Zhou;Jianjun Shi

  • State space modeling of dimensional variation propagation in multistage machining process using differential motion vectors

    Shiyu Zhou;Qiang Huang;Jianjun Shi

  • A Data-Level Fusion Model for Developing Composite Health Indices for Degradation Modeling and Prognostic Analysis

    Kaibo Liu;N. Z. Gebraeel;Jianjun Shi

  • Stream of Variation Modeling and Analysis for Multistage Manufacturing Processes

    Jianjun Shi

  • Fixture Failure Diagnosis for Autobody Assembly Using Pattern Recognition

    Darek Ceglarek;J. Shi

  • Quality control and improvement for multistage systems: A survey

    Jianjun Shi;Shiyu Zhou

  • Fault Diagnosis of Multistage Manufacturing Processes by Using State Space Approach

    Yu Ding;Dariusz Ceglarek;Jianjun Shi

  • Dimensional variation reduction for automotive body assembly

    Darek Ceglarek;J. Shi

  • Automatic feature extraction of waveform signals for in-process diagnostic performance improvement

    Jionghua Jin;Jianjun Shi

  • Feature-preserving data compression of stamping tonnage information using wavelets

    Jionghua Jin;Jianjun Shi

  • A survey on statistical methods for health care fraud detection

    Jing Li;Kuei Ying Huang;Jionghua Jin;Jianjun Shi

  • Diagnosability Analysis of Multi-Station Manufacturing Processes

    Yu Ding;Jianjun Shi;Dariusz Ceglarek

  • MODELING AND DIAGNOSIS OF MULTISTAGE MANUFACTURING PROCESSES: PART I - STATE SPACE MODEL

    Yu Ding;Dariusz Ceglarek;Jianjun Shi

  • Diagnosis of Multiple Fixture Faults in Panel Assembly

    D. W. Apley;J. Shi

  • The GLRT for statistical process control of autocorrelated processes

    Daniel W. Apley;Jianjun Shi

  • A Knowledge-Based Diagnostic Approach for the Launch of the Auto-Body Assembly Process

    Darek Ceglarek;J. Shi;S. M. Wu

  • Diagnosability Study of Multistage Manufacturing Processes Based on Linear Mixed-Effects Models

    Shiyu Zhou;Yu Ding;Yong Chen;Jianjun Shi

  • Image-Based Process Monitoring Using Low-Rank Tensor Decomposition

    Hao Yan;Kamran Paynabar;Jianjun Shi

  • A Factor-Analysis Method for Diagnosing Variability in Mulitvariate Manufacturing Processes

    Daniel W Apley;Jianjun Shi

Frequent Co-Authors

Darek Ceglarek
Darek Ceglarek University of Warwick
Shiyu Zhou
Shiyu Zhou University of Wisconsin–Madison
Yu Ding
Yu Ding Georgia Institute of Technology
S. Jack Hu
S. Jack Hu University of California, Riverside
Chuck Zhang
Chuck Zhang Georgia Institute of Technology
Ben Wang
Ben Wang Georgia Institute of Technology
Massimo Ruzzene
Massimo Ruzzene University of Colorado Boulder
Fugee Tsung
Fugee Tsung Hong Kong University of Science and Technology
Linkan Bian
Linkan Bian Mississippi State University
Elsayed A. Elsayed
Elsayed A. Elsayed Rutgers, The State University of New Jersey

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