World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
64
Citations
15655
World Ranking
2612
National Ranking
1298

Electronics and Electrical Engineering

D-Index
59
Citations
13324
World Ranking
1750
National Ranking
700

Yanzhi Wang 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 Yanzhi Wang 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: 446 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: 373 publications — 71st percentile

71% of scientists in this discipline score the same or lower.

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

Yanzhi Wang 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 Yanzhi Wang sits on this spectrum.

30 D-Index: 178 scientists 31 D-Index: 257 scientists 32 D-Index: 263 scientists 33 D-Index: 263 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: 59 D-Index — 75th percentile

75% of scientists in this discipline score the same or lower.

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

Overview

Yanzhi Wang is a researcher affiliated with Northeastern University in the United States, with a substantial body of work primarily centered in the fields of Computer Science and Engineering. Their publications reflect a focus on multiple subfields, including Computer Vision and Pattern Recognition, Artificial Intelligence, Electrical and Electronic Engineering, Materials Chemistry, and Renewable Energy, Sustainability and the Environment.

Their research topics cover diverse areas such as Advanced Neural Network Applications, Domain Adaptation and Few-Shot Learning, Advanced Memory and Neural Computing, Advanced Image and Video Retrieval Techniques, CCD and CMOS Imaging Sensors, Adversarial Robustness in Machine Learning, and Electrocatalysts for Energy Conversion.

Among their recent publications are:

  • EfficientFormer: Vision Transformers at MobileNet Speed, 2022, published in arXiv (Cornell University)
  • AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates, 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • A Survey of Stochastic Computing Neural Networks for Machine Learning Applications, 2020, IEEE Transactions on Neural Networks and Learning Systems
  • PCONV: The Missing but Desirable Sparsity in DNN Weight Pruning for Real-Time Execution on Mobile Devices, 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • Controlling Oxygen Reduction Selectivity through Steric Effects: Electrocatalytic Two-Electron and Four-Electron Oxygen Reduction with Cobalt Porphyrin Atropisomers, 2021, Angewandte Chemie International Edition

Yanzhi Wang collaborates frequently with a group of co-authors, including Wei Niu, Geng Yuan, Xue Lin, Yanyu Li, and Bin Ren. These collaborations contribute to a significant volume of work published in prominent venues.

Their frequent publication venues include:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
  • Angewandte Chemie
  • IEEE Transactions on Neural Networks and Learning Systems

Yanzhi Wang's body of work, spanning advanced neural network techniques, machine learning applications, and electrocatalysis, reflects an interdisciplinary approach bridging computer science and engineering disciplines. This multidisciplinary focus is evident across their publication record and their research topics addressing both theoretical and applied aspects of technology and materials science.

Best Publications

  • A Systematic DNN Weight Pruning Framework Using Alternating Direction Method of Multipliers

    Tianyun Zhang;Shaokai Ye;Kaiqi Zhang;Jian Tang

  • Experience-driven Networking: A Deep Reinforcement Learning based Approach

    Zhiyuan Xu;Jian Tang;Jingsong Meng;Weiyi Zhang

  • Deep Reinforcement Learning for Building HVAC Control

    Tianshu Wei;Yanzhi Wang;Qi Zhu

  • Spatiotemporal modeling and prediction in cellular networks: A big data enabled deep learning approach

    Jing Wang;Jian Tang;Zhiyuan Xu;Yanzhi Wang

  • PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning

    Wei Niu;Xiaolong Ma;Sheng Lin;Shihao Wang

  • Adversarial T-shirt! Evading Person Detectors in A Physical World

    Kaidi Xu;Gaoyuan Zhang;Sijia Liu;Quanfu Fan

  • A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning

    Ning Liu;Zhe Li;Jielong Xu;Zhiyuan Xu

  • Task Scheduling with Dynamic Voltage and Frequency Scaling for Energy Minimization in the Mobile Cloud Computing Environment

    Xue Lin;Yanzhi Wang;Qing Xie;Massoud Pedram

  • A deep reinforcement learning based framework for power-efficient resource allocation in cloud RANs

    Zhiyuan Xu;Yanzhi Wang;Jian Tang;Jing Wang

  • Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples

    Zihao Liu;Qi Liu;Tao Liu;Nuo Xu

  • CirCNN: accelerating and compressing deep neural networks using block-circulant weight matrices

    Caiwen Ding;Siyu Liao;Yanzhi Wang;Zhe Li

  • Multi-Channel Attention Selection GAN With Cascaded Semantic Guidance for Cross-View Image Translation

    Hao Tang;Dan Xu;Nicu Sebe;Yanzhi Wang

  • C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs

    Shuo Wang;Zhe Li;Caiwen Ding;Bo Yuan

  • AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates.

    Ning Liu;Xiaolong Ma;Zhiyuan Xu;Yanzhi Wang

  • PCONV: The Missing but Desirable Sparsity in DNN Weight Pruning for Real-Time Execution on Mobile Devices.

    Xiaolong Ma;Fu-Ming Guo;Wei Niu;Xue Lin

  • CirCNN: Accelerating and Compressing Deep Neural Networks Using Block-CirculantWeight Matrices

    Caiwen Ding;Siyu Liao;Yanzhi Wang;Zhe Li

  • A Survey of Stochastic Computing Neural Networks for Machine Learning Applications

    Yidong Liu;Siting Liu;Yanzhi Wang;Fabrizio Lombardi

  • SPViT: Enabling Faster Vision Transformers via Latency-Aware Soft Token Pruning

    Unknown

  • Adaptive Control for Energy Storage Systems in Households With Photovoltaic Modules

    Yanzhi Wang;Xue Lin;Massoud Pedram

  • SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing

    Ao Ren;Zhe Li;Caiwen Ding;Qinru Qiu

  • ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Methods of Multipliers

    Ao Ren;Tianyun Zhang;Shaokai Ye;Jiayu Li

  • Experience-Driven Congestion Control: When Multi-Path TCP Meets Deep Reinforcement Learning

    Zhiyuan Xu;Jian Tang;Chengxiang Yin;Yanzhi Wang

  • Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples

    Zihao Liu;Qi Liu;Tao Liu;Yanzhi Wang

  • SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing

    Ao Ren;Ji Li;Zhe Li;Caiwen Ding

Frequent Co-Authors

Massoud Pedram
Massoud Pedram University of Southern California
Naehyuck Chang
Naehyuck Chang Korea Advanced Institute of Science and Technology
Qinru Qiu
Qinru Qiu Syracuse University
Jian Tang
Jian Tang Syracuse University
Bo Yuan
Bo Yuan Rutgers, The State University of New Jersey
Sijia Liu
Sijia Liu Michigan State University
Bin Ren
Bin Ren Xiamen University
Bruce Allen
Bruce Allen Max Planck Society
N. A. Robertson
N. A. Robertson California Institute of Technology
Alessandra Buonanno
Alessandra Buonanno Max Planck Institute for Gravitational Physics

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