World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
62
Citations
15149
World Ranking
1884
National Ranking
606

Mingyi Hong 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 Mingyi Hong 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: 263 publications — 68th percentile

68% of scientists in this discipline score the same or lower.

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

Mingyi Hong 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 Mingyi Hong 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: 62 D-Index — 81st percentile

81% of scientists in this discipline score the same or lower.

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

Overview

Mingyi Hong is affiliated with the University of Minnesota in the United States. Their research primarily spans the fields of Computer Science and Engineering, with a focus on subfields such as Artificial Intelligence, Electrical and Electronic Engineering, Computational Mechanics, Computer Networks and Communications, and Computer Vision and Pattern Recognition.

Hong's work covers a range of topics including Sparse and Compressive Sensing Techniques, Stochastic Gradient Optimization Techniques, Privacy-Preserving Technologies in Data, Advanced MIMO Systems Optimization, Distributed Control Multi-Agent Systems, Adversarial Robustness in Machine Learning, and Advanced Optimization Algorithms Research.

Their recent papers reflect these research interests and include the following publications:

  • "Penalty Dual Decomposition Method for Nonsmooth Nonconvex Optimization-Part I: Algorithms and Convergence Analysis" (2020), IEEE Transactions on Signal Processing
  • "FedPD: A Federated Learning Framework With Adaptivity to Non-IID Data" (2021), IEEE Transactions on Signal Processing
  • "Hybrid Block Successive Approximation for One-Sided Non-Convex Min-Max Problems: Algorithms and Applications" (2020), IEEE Transactions on Signal Processing
  • "Nonconvex Min-Max Optimization: Applications, Challenges, and Recent Theoretical Advances" (2020), IEEE Signal Processing Magazine
  • "Dense Recurrent Neural Networks for Accelerated MRI: History-Cognizant Unrolling of Optimization Algorithms" (2020), IEEE Journal of Selected Topics in Signal Processing

Mingyi Hong maintains collaborations with several frequent coauthors, including:

  • Sijia Liu
  • Tsung-Hui Chang
  • Siliang Zeng
  • Prashant Khanduri
  • Alfredo García

The scientist's publications have appeared in multiple venues, prominently featuring:

  • arXiv (Cornell University)
  • IEEE Transactions on Signal Processing
  • SIAM Journal on Optimization
  • IEEE Signal Processing Magazine
  • IEEE Journal on Selected Areas in Communications

Best Publications

  • A UNIFIED CONVERGENCE ANALYSIS OF BLOCK SUCCESSIVE MINIMIZATION METHODS FOR NONSMOOTH OPTIMIZATION

    Meisam Razaviyayn;Mingyi Hong;Zhi Quan Luo

  • Convergence Analysis of Alternating Direction Method of Multipliers for a Family of Nonconvex Problems

    Mingyi Hong;Zhi Quan Luo;Meisam Razaviyayn

  • On the linear convergence of the alternating direction method of multipliers

    Mingyi Hong;Zhi-Quan Luo

  • Learning to Optimize: Training Deep Neural Networks for Interference Management

    Haoran Sun;Xiangyi Chen;Qingjiang Shi;Mingyi Hong

  • Towards K-means-friendly spaces: simultaneous deep learning and clustering

    Bo Yang;Xiao Fu;Nicholas D. Sidiropoulos;Mingyi Hong

  • Multi-Agent Distributed Optimization via Inexact Consensus ADMM

    Tsung Hui Chang;Mingyi Hong;Xiangfeng Wang

  • A Unified Algorithmic Framework for Block-Structured Optimization Involving Big Data: With applications in machine learning and signal processing

    Mingyi Hong;Meisam Razaviyayn;Zhi-Quan Luo;Jong-Shi Pang

  • Topology attack and defense for graph neural networks: An optimization perspective

    Kaidi Xu;Hongge Chen;Sijia Liu;Pin Yu Chen

  • A deep learning method for online capacity estimation of lithium-ion batteries

    Sheng Shen;Mohammadkazem Sadoughi;Xiangyi Chen;Mingyi Hong

  • Learning to optimize: Training deep neural networks for wireless resource management

    Haoran Sun;Xiangyi Chen;Qingjiang Shi;Mingyi Hong

  • Joint Base Station Clustering and Beamformer Design for Partial Coordinated Transmission in Heterogeneous Networks

    Mingyi Hong;Ruoyu Sun;H. Baligh;Zhi-Quan Luo

  • Transmit Solutions for MIMO Wiretap Channels using Alternating Optimization

    Qiang Li;Mingyi Hong;Hoi-To Wai;Ya-Feng Liu

  • Asynchronous Distributed ADMM for Large-Scale Optimization—Part I: Algorithm and Convergence Analysis

    Tsung-Hui Chang;Mingyi Hong;Wei-Cheng Liao;Xiangfeng Wang

  • Penalty Dual Decomposition Method for Nonsmooth Nonconvex Optimization—Part I: Algorithms and Convergence Analysis

    Qingjiang Shi;Mingyi Hong

  • On the convergence of a class of Adam-type algorithms for non-convex optimization

    Xiangyi Chen;Sijia Liu;Ruoyu Sun;Mingyi Hong

  • Iteration complexity analysis of block coordinate descent methods

    Mingyi Hong;Xiangfeng Wang;Meisam Razaviyayn;Zhi-Quan Luo

  • FedBCD: A Communication-Efficient Collaborative Learning Framework for Distributed Features

    Unknown

  • FedPD: A Federated Learning Framework With Adaptivity to Non-IID Data

    Xinwei Zhang;Mingyi Hong;Sairaj Dhople;Wotao Yin

  • Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization

    Hoi-To Wai;Zhuoran Yang;Zhaoran Wang;Mingyi Hong

  • Hybrid Block Successive Approximation for One-Sided Non-Convex Min-Max Problems: Algorithms and Applications

    Songtao Lu;Ioannis Tsaknakis;Mingyi Hong;Yongxin Chen

  • A Two-Timescale Framework for Bilevel Optimization: Complexity Analysis and Application to Actor-Critic

    Mingyi Hong;Hoi To Wai;Zhaoran Wang;Zhuoran Yang

  • Understanding Gradient Clipping in Private SGD: A Geometric Perspective

    Xiangyi Chen;Zhiwei Steven Wu;Mingyi Hong

  • FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data.

    Xinwei Zhang;Mingyi Hong;Sairaj V. Dhople;Wotao Yin

  • Joint Base Station Clustering and Beamformer Design for Partial Coordinated Transmission in Heterogenous Networks

    Mingyi Hong;Ruo-Yu Sun;Hadi Baligh;Zhi-Quan Luo

Frequent Co-Authors

Zhi-Quan Luo
Zhi-Quan Luo Chinese University of Hong Kong, Shenzhen
Meisam Razaviyayn
Meisam Razaviyayn University of Southern California
Tsung-Hui Chang
Tsung-Hui Chang Chinese University of Hong Kong, Shenzhen
Qingjiang Shi
Qingjiang Shi Tongji University
Sijia Liu
Sijia Liu Michigan State University
Nicholas D. Sidiropoulos
Nicholas D. Sidiropoulos University of Virginia
Jong-Shi Pang
Jong-Shi Pang University of Southern California
Han Liu
Han Liu Northwestern University
Yunlong Cai
Yunlong Cai Zhejiang University
Tao Zhang
Tao Zhang Chinese Academy of Sciences

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