World's Best Scientists 2026 revealed!

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
37
Citations
8603
World Ranking
5034
National Ranking
170

Computer Science

D-Index
41
Citations
9870
World Ranking
8670
National Ranking
86

Yung Yi 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 Yung Yi 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: 157 publications — 16th percentile

16% of scientists in this discipline score the same or lower.

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

Yung Yi 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 Yung Yi 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: 37 D-Index — 27th percentile

27% of scientists in this discipline score the same or lower.

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

Overview

Yung Yi is affiliated with the Korea Advanced Institute of Science and Technology in South Korea. Their research spans multiple areas within computer science and engineering, with a focus on applications including artificial intelligence, computer networks, and automotive engineering.

Their scholarly contributions cover a wide range of topics, such as:

  • Mobile Crowdsensing and Crowdsourcing
  • Complex Network Analysis Techniques
  • Auction Theory and Applications
  • Transportation and Mobility Innovations
  • Reinforcement Learning in Robotics
  • Domain Adaptation and Few-Shot Learning
  • Advanced Battery Technologies Research

Yung Yi has published extensively with notable frequent co-authors including Kyunghwan Son, Joonki Hong, Jinwoo Shin, Eui-Rim Jeong, and Jaeyoung Choi. These collaborative efforts have been disseminated through various academic outlets.

Their frequent publication venues include:

  • arXiv (Cornell University)
  • IEEE/ACM Transactions on Networking
  • Applied Energy
  • Journal of Medical Internet Research
  • IEEE Transactions on Mobile Computing

Selected recent papers authored or co-authored by Yung Yi are:

  • Towards the swift prediction of the remaining useful life of lithium-ion batteries with end-to-end deep learning, 2020, Applied Energy
  • Detection of Atrial Fibrillation Using a Ring-Type Wearable Device (CardioTracker) and Deep Learning Analysis of Photoplethysmography Signals: Prospective Observational Proof-of-Concept Study, 2020, Journal of Medical Internet Research
  • QTRAN++: Improved Value Transformation for Cooperative Multi-Agent Reinforcement Learning, 2020, arXiv (Cornell University)
  • Information Source Finding in Networks: Querying With Budgets, 2020, IEEE/ACM Transactions on Networking
  • On Self-configuring IoT with Dual Radios: A Cross-Layer Approach, 2021, IEEE Transactions on Mobile Computing

Their research projects typically employ techniques in reinforcement learning, network analysis, and deep learning, addressing problems in areas such as battery life prediction, health monitoring through wearable devices, multi-agent systems, and Internet of Things configuration strategies.

Yung Yi's main fields of study are Computer Science and Engineering, with 17 and 7 publications respectively contributing to these domains. Their subfields include:

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Automotive Engineering
  • Management Science and Operations Research

Best Publications

  • Rethinking virtual network embedding: substrate support for path splitting and migration

    Minlan Yu;Yung Yi;Jennifer Rexford;Mung Chiang

  • Mobile data offloading: how much can WiFi deliver?

    Kyunghan Lee;Joohyun Lee;Yung Yi;Injong Rhee

  • Base Station Operation and User Association Mechanisms for Energy-Delay Tradeoffs in Green Cellular Networks

    Kyuho Son;Hongseok Kim;Yung Yi;B. Krishnamachari

  • Mobile data offloading: how much can WiFi deliver?

    Kyunghan Lee;Joohyun Lee;Yung Yi;Injong Rhee

  • Hop-by-hop congestion control over a wireless multi-hop network

    Yung Yi;S. Shakkottai

  • Hop-by-hop congestion control over a wireless multi-hop network

    Yung Yi;Sanjay Shakkottai

  • QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning.

    Kyunghwan Son;Daewoo Kim;Wan Ju Kang;David Earl Hostallero

  • TEAR: TCP emulation at receivers – flow control for multimedia streaming

    Injong Rhee;Volkan Ozdemir;Yung Yi

  • Towards the swift prediction of the remaining useful life of lithium-ion batteries with end-to-end deep learning

    Joonki Hong;Dongheon Lee;Eui-Rim Jeong;Yung Yi

  • REFIM: A Practical Interference Management in Heterogeneous Wireless Access Networks

    Kyuho Son;Soohwan Lee;Yung Yi;Song Chong

  • Kargus: a highly-scalable software-based intrusion detection system

    Muhammad Asim Jamshed;Jihyung Lee;Sangwoo Moon;Insu Yun

  • Towards utility-optimal random access without message passing

    J. Liu;Y. Yi;A. Proutiere;M. Chiang

  • Stochastic network utility maximisation—a tribute to Kelly's paper published in this journal a decade ago

    Yung Yi;Mung Chiang

  • Economics of WiFi Offloading: Trading Delay for Cellular Capacity

    Joohyun Lee;Yung Yi;Song Chong;Youngmi Jin

  • Max-Contribution: On Optimal Resource Allocation in Delay Tolerant Networks

    Kyunghan Lee;Yung Yi;Jaeseong Jeong;Hyungsuk Won

  • The Economic Effects of Sharing Femtocells

    Se-Young Yun;Yung Yi;Dong-Ho Cho;Jeonghoon Mo

  • How Much and When Do We Need Higher-order Information in Hypergraphs? A Case Study on Hyperedge Prediction

    Se-eun Yoon;Hyungseok Song;Kijung Shin;Yung Yi

  • Learning to Schedule Communication in Multi-agent Reinforcement Learning

    Daewoo Kim;Sangwoo Moon;David Hostallero;Wan Ju Kang

  • Complexity in wireless scheduling: impact and tradeoffs

    Yung Yi;Alexandre Proutière;Mung Chiang

  • Deep Learning Approaches to Detect Atrial Fibrillation Using Photoplethysmographic Signals: Algorithms Development Study

    Soonil Kwon;Joonki Hong;Eue Keun Choi;Euijae Lee

  • Open or close: On the sharing of femtocells

    Seyoung Yun;Yung Yi;Dong-Ho Cho;Jeonghoon Mo

  • MAC scheduling with low overheads by learning neighborhood contention patterns

    Yung Yi;Gustavo de Veciana;Sanjay Shakkottai

Frequent Co-Authors

Jinwoo Shin
Jinwoo Shin Korea Advanced Institute of Science and Technology
Song Chong
Song Chong Korea Advanced Institute of Science and Technology
Mung Chiang
Mung Chiang Purdue University West Lafayette
Alexandre Proutiere
Alexandre Proutiere Royal Institute of Technology
Injong Rhee
Injong Rhee North Carolina State University
Sanjay Shakkottai
Sanjay Shakkottai The University of Texas at Austin
Marc Moonen
Marc Moonen KU Leuven
Sewoong Oh
Sewoong Oh University of Washington
Yanghee Choi
Yanghee Choi Seoul National University
Jae-Young Choi
Jae-Young Choi Sungkyunkwan University

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