World's Best Scientists 2026 revealed!

D-Index & Metrics

Social Sciences and Humanities

D-Index
53
Citations
9992
World Ranking
2317
National Ranking
27

Engineering and Technology

D-Index
51
Citations
9158
World Ranking
3897
National Ranking
783

Jie Wu publication distribution in Social Sciences and Humanities in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Social Sciences and Humanities in 2026. The highlighted bar marks where Jie Wu sits on this spectrum.

44–53 publications: 28 scientists 54–63 publications: 61 scientists 64–73 publications: 161 scientists 74–83 publications: 225 scientists 84–93 publications: 301 scientists 94–103 publications: 390 scientists 104–113 publications: 406 scientists 114–123 publications: 474 scientists 124–133 publications: 453 scientists 134–143 publications: 434 scientists 144–153 publications: 407 scientists 154–163 publications: 411 scientists 164–173 publications: 391 scientists 174–183 publications: 313 scientists 184–193 publications: 319 scientists 194–203 publications: 304 scientists 204–213 publications: 285 scientists 214–223 publications: 246 scientists 224–233 publications: 213 scientists 234–243 publications: 170 scientists 244–253 publications: 167 scientists 254–263 publications: 162 scientists 264–273 publications: 138 scientists 274–283 publications: 138 scientists 284–293 publications: 113 scientists 294–303 publications: 106 scientists 304–313 publications: 95 scientists 314–323 publications: 101 scientists 324–333 publications: 81 scientists 334–343 publications: 69 scientists 344–353 publications: 56 scientists 354–363 publications: 36 scientists 364–373 publications: 42 scientists 374–383 publications: 41 scientists 384–393 publications: 44 scientists 394–403 publications: 27 scientists 404–413 publications: 30 scientists 414–423 publications: 20 scientists 424–433 publications: 29 scientists 434–443 publications: 29 scientists 444–453 publications: 25 scientists 454–463 publications: 19 scientists 464–473 publications: 21 scientists 474–483 publications: 11 scientists 484–493 publications: 17 scientists 494–503 publications: 7 scientists 504–513 publications: 6 scientists 514–523 publications: 12 scientists 524–533 publications: 10 scientists 534–543 publications: 7 scientists 544–553 publications: 6 scientists 554–563 publications: 12 scientists 564–569 publications: 8 scientists 570+ publications: 100 scientists
44 publications 570+

This scientist: 174 publications — 54th percentile

54% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 570 publications or more.

Jie Wu D-index placement in Social Sciences and Humanities in 2026

The chart shows the D-index (discipline H-index) distribution of Social Sciences and Humanities scientists ranked by Research.com in 2026. The highlighted bar marks where Jie Wu sits on this spectrum.

30 D-Index: 177 scientists 31 D-Index: 210 scientists 32 D-Index: 222 scientists 33 D-Index: 188 scientists 34 D-Index: 181 scientists 35 D-Index: 243 scientists 36 D-Index: 261 scientists 37 D-Index: 272 scientists 38 D-Index: 278 scientists 39 D-Index: 300 scientists 40 D-Index: 297 scientists 41 D-Index: 308 scientists 42 D-Index: 313 scientists 43 D-Index: 300 scientists 44 D-Index: 269 scientists 45 D-Index: 260 scientists 46 D-Index: 228 scientists 47 D-Index: 251 scientists 48 D-Index: 203 scientists 49 D-Index: 186 scientists 50 D-Index: 187 scientists 51 D-Index: 166 scientists 52 D-Index: 138 scientists 53 D-Index: 141 scientists 54 D-Index: 131 scientists 55 D-Index: 137 scientists 56 D-Index: 109 scientists 57 D-Index: 129 scientists 58 D-Index: 95 scientists 59 D-Index: 105 scientists 60 D-Index: 106 scientists 61 D-Index: 86 scientists 62 D-Index: 94 scientists 63 D-Index: 77 scientists 64 D-Index: 79 scientists 65 D-Index: 59 scientists 66 D-Index: 68 scientists 67 D-Index: 51 scientists 68 D-Index: 49 scientists 69 D-Index: 40 scientists 70 D-Index: 43 scientists 71 D-Index: 40 scientists 72 D-Index: 50 scientists 73 D-Index: 46 scientists 74 D-Index: 43 scientists 75 D-Index: 45 scientists 76 D-Index: 45 scientists 77 D-Index: 30 scientists 78 D-Index: 25 scientists 79 D-Index: 31 scientists 80 D-Index: 29 scientists 81 D-Index: 18 scientists 82 D-Index: 21 scientists 83 D-Index: 26 scientists 84 D-Index: 15 scientists 85 D-Index: 15 scientists 86 D-Index: 19 scientists 87 D-Index: 19 scientists 88 D-Index: 12 scientists 89 D-Index: 7 scientists 90 D-Index: 19 scientists 91 D-Index: 20 scientists 92 D-Index: 13 scientists 93 D-Index: 9 scientists 94 D-Index: 13 scientists 95 D-Index: 8 scientists 96 D-Index: 7 scientists 97 D-Index: 6 scientists 98 D-Index: 6 scientists 99 D-Index: 6 scientists 100+ D-Index: 97 scientists
30 D-Index 100+

This scientist: 53 D-Index — 72nd percentile

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

The last bar groups every scientist with 100 D-Index or more.

Research.com Recognitions

  • 2018 - Fellow of the American Association for the Advancement of Science (AAAS)

Overview

Jie Wu is affiliated with the University of Science and Technology of China and has contributed significantly to the fields of computer science and engineering. Their research work encompasses multiple subfields, including computer networks and communications, control and systems engineering, electrical and electronic engineering, statistics and probability, and computational theory and mathematics.

Key research topics Jie Wu has focused on include:

  • Distributed Control Multi-Agent Systems
  • Neural Networks Stability and Synchronization
  • Adaptive Control of Nonlinear Systems
  • Stability and Control of Uncertain Systems
  • Adaptive Dynamic Programming Control
  • Smart Grid Security and Resilience
  • Opportunistic and Delay-Tolerant Networks

Their recent published papers demonstrate diverse interests and include:

  • "Solar energy potential of urban buildings in 10 cities of China," 2020, published in Energy
  • "Consensus of second-order heterogeneous multi-agent systems with and without input saturation," 2021, published in ISA Transactions
  • "Fixed-time Group Consensus of Nonlinear Multi-agent Systems via Pinning Control," 2020, published in International Journal of Control Automation and Systems
  • "Observer-Based Adaptive Time-Varying Formation-Containment Tracking for Multiagent System With Bounded Unknown Input," 2022, published in IEEE Transactions on Systems Man and Cybernetics Systems
  • "Distributed bipartite tracking consensus of nonlinear multi-agent systems with quantized communication," 2020, published in Neurocomputing

Jie Wu has collaborated with various researchers. Frequent co-authors include:

  • Xisheng Zhan
  • Huaicheng Yan
  • Tao Han
  • Qingsheng Yang
  • Xiaoyi Zhang

The scientist has published extensively in notable venues such as:

  • IEEE Transactions on Circuits & Systems II Express Briefs
  • International Journal of Control Automation and Systems
  • IEEE Access
  • IEEE Transactions on Systems Man and Cybernetics Systems
  • Neurocomputing

Jie Wu's contributions are primarily within computer science and engineering, with 68 and 56 publications respectively. Their work spans technical areas with a focus on distributed multi-agent systems, neural networks, and adaptive control methodologies.

In 2018, Jie Wu was recognized as a Fellow of the American Association for the Advancement of Science (AAAS).

Best Publications

  • The DEA Game Cross-Efficiency Model and Its Nash Equilibrium

    Liang Liang;Jie Wu;Wade D. Cook;Joe Zhu

  • Efficiency measures of the Chinese commercial banking system using an additive two-stage DEA

    Ke Wang;Wei Huang;Jie Wu;Ying-Nan Liu

  • Environmental efficiency evaluation based on data envelopment analysis: A review

    Malin Song;Qingxian An;Wei Zhang;Zeya Wang

  • Alternative secondary goals in DEA cross-efficiency evaluation

    Liang Liang;Jie Wu;Wade D. Cook;Joe Zhu

  • Determination of weights for ultimate cross efficiency using Shannon entropy

    Jie Wu;Jiasen Sun;Liang Liang;Yingchun Zha

  • DEA game cross-efficiency approach to Olympic rankings

    Jie Wu;Liang Liang;Yao Chen

  • Measuring energy and environmental efficiency of transportation systems in China based on a parallel DEA approach

    Jie Wu;Qingyuan Zhu;Junfei Chu;Hongwei Liu;Hongwei Liu

  • Achievement and benchmarking of countries at the Summer Olympics using cross efficiency evaluation method

    Jie Wu;Liang Liang;Feng Yang;Feng Yang

  • Energy and environmental efficiency measurement of China's industrial sectors: A DEA model with non-homogeneous inputs and outputs

    Jie Wu;Mingjun Li;Qingyuan Zhu;Zhixiang Zhou

  • CO2 emissions and energy intensity reduction allocation over provincial industrial sectors in China

    Jie Wu;Qingyuan Zhu;Liang Liang

  • A comprehensive analysis of China's regional energy saving and emission reduction efficiency: From production and treatment perspectives

    Jie Wu;Lin Lv;Jiasen Sun;Xiang Ji

  • DEA cross-efficiency evaluation based on Pareto improvement

    Jie Wu;Junfei Chu;Jiasen Sun;Qingyuan Zhu

  • Two-stage network processes with shared resources and resources recovered from undesirable outputs

    Jie Wu;Qingyuan Zhu;Xiang Ji;Junfei Chu

  • Determination of the weights for the ultimate cross efficiency using Shapley value in cooperative game

    Jie Wu;Liang Liang;Feng Yang

  • Evaluating the environmental efficiency of a two-stage system with undesired outputs by a DEA approach: An interest preference perspective

    Jie Wu;Pengzhen Yin;Jiasen Sun;Junfei Chu

  • Cross efficiency evaluation method based on weight-balanced data envelopment analysis model

    Jie Wu;Jiasen Sun;Liang Liang

  • Environmental efficiency evaluation of industry in China based on a new fixed sum undesirable output data envelopment analysis

    Jie Wu;Qingxian An;Qingxian An;Xin Yao;Bo Wang

  • Performance evaluation of Chinese port enterprises under significant environmental concerns: An extended DEA-based analysis

    Jiasen Sun;Yang Yuan;Rui Yang;Xiang Ji

  • Total-factor energy efficiency evaluation of Chinese industry by using two-stage DEA model with shared inputs

    Jie Wu;Beibei Xiong;Qingxian An;Jiasen Sun

  • Performance ranking of units considering ideal and anti-ideal DMU with common weights

    Jiasen Sun;Jie Wu;Dong Guo

  • To reduce energy consumption and to maintain rapid economic growth: Analysis of the condition in China based on expended IPAT model

    Malin Song;Shuhong Wang;Huayin Yu;Li Yang

  • Extended secondary goal models for weights selection in DEA cross-efficiency evaluation

    Jie Wu;Junfei Chu;Jiasen Sun;Qingyuan Zhu

  • Energy and environmental efficiency of China's transportation sectors under the constraints of energy consumption and environmental pollutions

    Qingyuan Zhu;Xingchen Li;Feng Li;Jie Wu

Frequent Co-Authors

Liang Liang
Liang Liang Hefei University of Technology
Malin Song
Malin Song Anhui University of Finance and Economics
Wade D. Cook
Wade D. Cook York University
Joe Zhu
Joe Zhu Worcester Polytechnic Institute
Feng Li
Feng Li City, University of London
George Q. Huang
George Q. Huang Hong Kong Polytechnic University
Ray Y. Zhong
Ray Y. Zhong University of Hong Kong
Chengbin Chu
Chengbin Chu École Supérieure d'Ingénieurs en Électrotechnique et Électronique
Dequn Zhou
Dequn Zhou Nanjing University of Aeronautics and Astronautics

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