World's Best Scientists 2026 revealed!

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
85
Citations
28292
World Ranking
384
National Ranking
17

Biao Huang publication distribution in Electronics and Electrical Engineering in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Electronics and Electrical Engineering in 2026. The highlighted bar marks where Biao Huang sits on this spectrum.

34–53 publications: 24 scientists 54–73 publications: 52 scientists 74–93 publications: 114 scientists 94–113 publications: 203 scientists 114–133 publications: 269 scientists 134–153 publications: 355 scientists 154–173 publications: 403 scientists 174–193 publications: 445 scientists 194–213 publications: 430 scientists 214–233 publications: 431 scientists 234–253 publications: 399 scientists 254–273 publications: 366 scientists 274–293 publications: 335 scientists 294–313 publications: 300 scientists 314–333 publications: 276 scientists 334–353 publications: 250 scientists 354–373 publications: 214 scientists 374–393 publications: 187 scientists 394–413 publications: 152 scientists 414–433 publications: 169 scientists 434–453 publications: 147 scientists 454–473 publications: 111 scientists 474–493 publications: 117 scientists 494–513 publications: 103 scientists 514–533 publications: 99 scientists 534–553 publications: 92 scientists 554–573 publications: 75 scientists 574–593 publications: 58 scientists 594–613 publications: 69 scientists 614–633 publications: 50 scientists 634–653 publications: 62 scientists 654–673 publications: 54 scientists 674–693 publications: 44 scientists 694–713 publications: 37 scientists 714–733 publications: 28 scientists 734–753 publications: 26 scientists 754–773 publications: 26 scientists 774–793 publications: 19 scientists 794–813 publications: 23 scientists 814–833 publications: 20 scientists 834–853 publications: 16 scientists 854–873 publications: 20 scientists 874–893 publications: 11 scientists 894–913 publications: 11 scientists 914–933 publications: 16 scientists 934–953 publications: 13 scientists 954–973 publications: 10 scientists 974–993 publications: 11 scientists 994–1,013 publications: 9 scientists 1,014–1,033 publications: 9 scientists 1,034–1,053 publications: 10 scientists 1,054–1,064 publications: 6 scientists 1,065+ publications: 99 scientists
34 publications 1,065+

This scientist: 643 publications — 92nd percentile

92% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 1,065 publications or more.

Biao Huang D-index placement in Electronics and Electrical Engineering in 2026

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

30 D-Index: 178 scientists 31 D-Index: 257 scientists 32 D-Index: 263 scientists 33 D-Index: 262 scientists 34 D-Index: 244 scientists 35 D-Index: 236 scientists 36 D-Index: 211 scientists 37 D-Index: 220 scientists 38 D-Index: 214 scientists 39 D-Index: 214 scientists 40 D-Index: 205 scientists 41 D-Index: 187 scientists 42 D-Index: 194 scientists 43 D-Index: 201 scientists 44 D-Index: 155 scientists 45 D-Index: 189 scientists 46 D-Index: 148 scientists 47 D-Index: 160 scientists 48 D-Index: 134 scientists 49 D-Index: 130 scientists 50 D-Index: 141 scientists 51 D-Index: 156 scientists 52 D-Index: 108 scientists 53 D-Index: 130 scientists 54 D-Index: 112 scientists 55 D-Index: 97 scientists 56 D-Index: 111 scientists 57 D-Index: 102 scientists 58 D-Index: 108 scientists 59 D-Index: 120 scientists 60 D-Index: 103 scientists 61 D-Index: 93 scientists 62 D-Index: 92 scientists 63 D-Index: 74 scientists 64 D-Index: 77 scientists 65 D-Index: 73 scientists 66 D-Index: 64 scientists 67 D-Index: 69 scientists 68 D-Index: 60 scientists 69 D-Index: 39 scientists 70 D-Index: 57 scientists 71 D-Index: 59 scientists 72 D-Index: 46 scientists 73 D-Index: 49 scientists 74 D-Index: 38 scientists 75 D-Index: 35 scientists 76 D-Index: 32 scientists 77 D-Index: 35 scientists 78 D-Index: 31 scientists 79 D-Index: 22 scientists 80 D-Index: 34 scientists 81 D-Index: 31 scientists 82 D-Index: 34 scientists 83 D-Index: 23 scientists 84 D-Index: 18 scientists 85 D-Index: 30 scientists 86 D-Index: 19 scientists 87 D-Index: 19 scientists 88 D-Index: 20 scientists 89 D-Index: 8 scientists 90 D-Index: 17 scientists 91 D-Index: 7 scientists 92 D-Index: 14 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 12 scientists 97 D-Index: 10 scientists 98 D-Index: 10 scientists 99 D-Index: 12 scientists 100 D-Index: 16 scientists 101 D-Index: 5 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 8 scientists 105 D-Index: 9 scientists 106 D-Index: 13 scientists 107 D-Index: 4 scientists 108 D-Index: 5 scientists 109 D-Index: 10 scientists 110 D-Index: 8 scientists 111+ D-Index: 96 scientists
30 D-Index 111+

This scientist: 85 D-Index — 95th percentile

95% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 111 D-Index or more.

Research.com Recognitions

  • 2018 - IEEE Fellow For contributions to and application of Bayesian soft-sensing for control performance monitoring

Overview

Biao Huang is affiliated with the University of Alberta in Canada. Their research is primarily situated within the field of Engineering, with a particular focus on Control and Systems Engineering, which accounts for the majority of their publications. Additional subfields of study include Artificial Intelligence, Mechanical Engineering, Statistics, Probability and Uncertainty, and Electrical and Electronic Engineering.

The scientist's work spans a variety of main research topics, including:

  • Fault Detection and Control Systems
  • Advanced Control Systems Optimization
  • Control Systems and Identification
  • Mineral Processing and Grinding
  • Advanced Statistical Process Monitoring
  • Spectroscopy and Chemometric Analyses
  • Iterative Learning Control Systems

Biao Huang has published numerous papers in several key venues, reflecting consistent engagement with journals focused on chemical engineering and control systems. Frequent publication venues include:

  • Computers & Chemical Engineering
  • Journal of Process Control
  • SSRN Electronic Journal
  • IEEE Transactions on Cybernetics
  • arXiv (Cornell University)

Some of the recent papers authored or co-authored by Biao Huang are as follows:

  • "A review On reinforcement learning: Introduction and applications in industrial process control," 2020, Computers & Chemical Engineering
  • "Data-Driven Fault Diagnosis for Traction Systems in High-Speed Trains: A Survey, Challenges, and Perspectives," 2020, IEEE Transactions on Intelligent Transportation Systems
  • "Explainable Intelligent Fault Diagnosis for Nonlinear Dynamic Systems: From Unsupervised to Supervised Learning," 2022, IEEE Transactions on Neural Networks and Learning Systems
  • "Transfer Learning-Motivated Intelligent Fault Diagnosis Designs: A Survey, Insights, and Perspectives," 2023, IEEE Transactions on Neural Networks and Learning Systems
  • "Reinforcement learning approach to autonomous PID tuning," 2022, Computers & Chemical Engineering

The scientist has collaborated extensively with several frequent co-authors, notably:

  • Ronghu Chi
  • Chunhui Zhao
  • Hongtian Chen
  • Zhongsheng Hou
  • Ranjith Chiplunkar

Biao Huang was awarded the IEEE Fellow distinction in 2018 for contributions to and applications of Bayesian soft-sensing for control performance monitoring.

Best Publications

  • A new method for stabilization of networked control systems with random delays

    Liqian Zhang;Yang Shi;Tongwen Chen;Biao Huang

  • Data Mining and Analytics in the Process Industry: The Role of Machine Learning

    Zhiqiang Ge;Zhihuan Song;Steven X. Ding;Biao Huang

  • Performance Assessment of Control Loops: Theory and Applications

    Biao Huang;S. L. Shah;M. A. Johnson;M. J. Grimble

  • A review On reinforcement learning: Introduction and applications in industrial process control

    Rui Nian;Jinfeng Liu;Biao Huang

  • Deep Learning-Based Feature Representation and Its Application for Soft Sensor Modeling With Variable-Wise Weighted SAE

    Xiaofeng Yuan;Biao Huang;Yalin Wang;Chunhua Yang

  • Performance Assessment of Control Loops

    Biao Huang;Sirish L. Shah

  • Data-Driven Fault Diagnosis for Traction Systems in High-Speed Trains: A Survey, Challenges, and Perspectives

    Hongtian Chen;Bin Jiang;Steven X. Ding;Biao Huang

  • Dynamic Modeling, Predictive Control and Performance Monitoring: A Data-driven Subspace Approach

    Biao Huang;Ramesh Kadali

  • Performance-Driven Distributed PCA Process Monitoring Based on Fault-Relevant Variable Selection and Bayesian Inference

    Qingchao Jiang;Xuefeng Yan;Biao Huang

  • Subspace method aided data-driven design of fault detection and isolation systems

    S.X. Ding;P. Zhang;A. Naik;E.L. Ding

  • Good, bad or optimal? Performance assessment of multivariable processes

    B. Huang;S. L. Shah;E. K. Kwok

  • Detection of multiple oscillations in control loops

    N.F Thornhill;B Huang;H Zhang

  • Review and Perspectives of Data-Driven Distributed Monitoring for Industrial Plant-Wide Processes

    Qingchao Jiang;Xuefeng Yan;Biao Huang

  • Design of inferential sensors in the process industry: A review of Bayesian methods

    Shima Khatibisepehr;Biao Huang;Swanand Khare

  • A full-condition monitoring method for nonstationary dynamic chemical processes with cointegration and slow feature analysis

    Chunhui Zhao;Biao Huang

  • Hierarchical Quality-Relevant Feature Representation for Soft Sensor Modeling: A Novel Deep Learning Strategy

    Xiaofeng Yuan;Jiao Zhou;Biao Huang;Yalin Wang

  • Closed-loop subspace identification: an orthogonal projection approach

    Biao Huang;Steven X. Ding;S.Joe Qin

  • A data driven subspace approach to predictive controller design

    Ramesh Kadali;Biao Huang;Anthony Rossiter

  • Survey on the theoretical research and engineering applications of multivariate statistics process monitoring algorithms: 2008–2017

    Youqing Wang;Yabin Si;Biao Huang;Zhijiang Lou

  • H∞ model reduction of Markovian jump linear systems☆

    Liqian Zhang;Biao Huang;James Lam

Frequent Co-Authors

Sirish L. Shah
Sirish L. Shah University of Alberta
Fei Liu
Fei Liu Jiangnan University
Ronghu Chi
Ronghu Chi Qingdao University of Science and Technology
Tongwen Chen
Tongwen Chen University of Alberta
Steven X. Ding
Steven X. Ding University of Duisburg-Essen
Zhongsheng Hou
Zhongsheng Hou Qingdao University
Zidong Wang
Zidong Wang Brunel University London
Chunhui Zhao
Chunhui Zhao Zhejiang University
Jinfeng Liu
Jinfeng Liu University of Alberta
Nina F. Thornhill
Nina F. Thornhill Imperial College London

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

For students pursuing Electronics and Electrical Engineering in the USA, exploring related online degrees can open diverse career opportunities. For those considering leadership roles in tech projects, earning a bachelor's in project management offers essential skills to manage complex engineering initiatives effectively.

Many professionals balance work and study, making bachelor degree programs for working adults a practical choice to advance their qualifications without pausing their careers. Furthermore, for those eager to complete their education quickly, finding a project management degree online fast allows accelerated learning pathways, enabling faster entry or promotion in the engineering field.

Electronics and Electrical Engineering professionals often thrive in roles suited to focused, independent work. Exploring good jobs for introverts can highlight specialized career paths where technical expertise and analytical skills are highly valued, complementing engineering backgrounds.

Overall, combining an engineering degree with project management education and understanding introvert-friendly careers can provide a balanced and strategic approach to career growth in the tech industry.

Best Scientists Citing Biao Huang

Trending Scientists

Recently Published Articles