World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
64
Citations
19484
World Ranking
1610
National Ranking
528

David W. Coit 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 David W. Coit sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 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: 118 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: 60 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: 233 publications — 60th percentile

60% of scientists in this discipline score the same or lower.

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

David W. Coit 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 David W. Coit sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 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: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 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: 95 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: 25 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: 64 D-Index — 84th percentile

84% of scientists in this discipline score the same or lower.

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

Overview

David W. Coit is affiliated with Rutgers, The State University of New Jersey in the United States. Their research primarily spans the field of Engineering, with a significant focus on specialized subfields including Safety, Risk, Reliability and Quality, Electrical and Electronic Engineering, Statistics, Probability and Uncertainty, Software, and Civil and Structural Engineering.

The scientist's main research topics include:

  • Reliability and Maintenance Optimization
  • Software Reliability and Analysis Research
  • Risk and Safety Analysis
  • Machine Fault Diagnosis Techniques
  • Statistical Distribution Estimation and Applications
  • Advanced Battery Technologies Research
  • Probabilistic and Robust Engineering Design

Frequent coauthors that have collaborated extensively with David W. Coit include Nooshin Yousefi, Jian Zhou, Stamatis Tsianikas, Frank A. Felder, and Zhanhang Li.

Their recent academic publications are as follows:

  • A review of Pareto pruning methods for multi-objective optimization, 2022, Computers & Industrial Engineering
  • Reinforcement learning for dynamic condition-based maintenance of a system with individually repairable components, 2020, Quality Engineering
  • Resiliency-based restoration optimization for dependent network systems against cascading failures, 2020, Reliability Engineering & System Safety
  • Reliability analysis of systems considering clusters of dependent degrading components, 2020, Reliability Engineering & System Safety
  • Dependent failure behavior modeling for risk and reliability: A systematic and critical literature review, 2023, Reliability Engineering & System Safety

David W. Coit's research outputs have appeared frequently in several publication venues, with multiple contributions to Reliability Engineering & System Safety, Computers & Industrial Engineering, arXiv (Cornell University), IISE Transactions, and IEEE Transactions on Reliability.

Best Publications

  • Multi-objective optimization using genetic algorithms: A tutorial

    Abdullah Konak;David W. Coit;Alice E. Smith

  • Reliability optimization of series-parallel systems using a genetic algorithm

    D.W. Coit;A.E. Smith

  • Reliability and maintenance modeling for systems subjected to multiple dependent competing failure precesses

    H Hao Peng;Q Qianmei Feng;DW David Coit

  • A Monte-Carlo simulation approach for approximating multi-state two-terminal reliability

    Jose Emmanuel Ramirez-Marquez;David W. Coit

  • EFFICIENTLY SOLVING THE REDUNDANCY ALLOCATION PROBLEM USING TABU SEARCH

    Sadan Kulturel-Konak;Alice E. Smith;David W. Coit

  • Penalty guided genetic search for reliability design optimization

    David W. Coit;Alice E. Smith

  • Adaptive Penalty Methods for Genetic Optimization of Constrained Combinatorial Problems

    David W. Coit;Alice E. Smith;David M. Tate

  • Cold-standby redundancy optimization for nonrepairable systems

    David W. Coit

  • Reliability Analysis for Multi-Component Systems Subject to Multiple Dependent Competing Failure Processes

    Sanling Song;David W. Coit;Qianmei Feng;Hao Peng

  • Composite importance measures for multi-state systems with multi-state components

    J.E. Ramirez-Marquez;D.W. Coit

  • Reliability modeling for dependent competing failure processes with changing degradation rate

    Koosha Rafiee;Qianmei Feng;David W. Coit

  • Maximization of System Reliability with a Choice of Redundancy Strategies

    David W. Coit

  • A HEURISTIC FOR SOLVING THE REDUNDANCY ALLOCATION PROBLEM FOR MULTI-STATE SERIES–PARALLEL SYSTEMS

    Jose Emmanuel Ramirez-Marquez;David W. Coit

  • SYSTEM RELIABILITY OPTIMIZATION WITH k-OUT-OF-n SUBSYSTEMS

    David W. Coit;Jia Chen Liu

  • Practical solutions for multi-objective optimization: An application to system reliability design problems

    Heidi A. Taboada;Fatema Baheranwala;David W. Coit;Naruemon Wattanapongsakorn

  • Reliability and Maintenance Modeling for Dependent Competing Failure Processes With Shifting Failure Thresholds

    Lei Jiang;Qianmei Feng;D. W. Coit

  • MOMS-GA: A Multi-Objective Multi-State Genetic Algorithm for System Reliability Optimization Design Problems

    H.A. Taboada;J.F. Espiritu;D.W. Coit

  • A review of Pareto pruning methods for multi-objective optimization

    Unknown

  • Multi-period multi-objective electricity generation expansion planning problem with Monte-Carlo simulation

    Hatice Tekiner;David W. Coit;Frank A. Felder

  • Joint optimization of production scheduling and machine group preventive maintenance

    Lei Xiao;Lei Xiao;Sanling Song;Xiaohui Chen;David W. Coit

  • Condition-based maintenance for continuously monitored degrading systems with multiple failure modes

    Xiao Liu;Jingrui Li;Khalifa N. Al-Khalifa;Abdelmagid S. Hamouda

  • Genetic Algorithms and Engineering Design

    Unknown

Frequent Co-Authors

Alice E. Smith
Alice E. Smith Auburn University
Jose Emmanuel Ramirez-Marquez
Jose Emmanuel Ramirez-Marquez Stevens Institute of Technology
Elsayed A. Elsayed
Elsayed A. Elsayed Rutgers, The State University of New Jersey
Enrico Zio
Enrico Zio Polytechnic University of Milan
Lirong Cui
Lirong Cui Beijing Institute of Technology
Annmarie G. Carlton
Annmarie G. Carlton University of California, Irvine
Liudong Xing
Liudong Xing University of Massachusetts Dartmouth
Abdel Magid Hamouda
Abdel Magid Hamouda Qatar University
Gregory Levitin
Gregory Levitin Southwest Jiaotong University
Honggang Wang
Honggang Wang University of Massachusetts Dartmouth

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