World's Best Scientists 2026 revealed!
Sicheng Zhao

Sicheng Zhao

D-Index & Metrics

Computer Science

D-Index
49
Citations
10890
World Ranking
5837
National Ranking
776

Sicheng Zhao publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Sicheng Zhao sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 140 publications — 23rd percentile

23% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Sicheng Zhao D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Sicheng Zhao sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 49 D-Index — 60th percentile

60% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Sicheng Zhao is affiliated with Tsinghua University in China and has contributed extensively to the field of computer science, with a particular focus on computer vision and pattern recognition as well as artificial intelligence. Their research spans multiple subfields, emphasizing computer vision, multimodal machine learning, domain adaptation, and sentiment analysis.

Their primary research areas include:

  • Multimodal Machine Learning Applications
  • Domain Adaptation and Few-Shot Learning
  • Advanced Image and Video Retrieval Techniques
  • Sentiment Analysis and Opinion Mining
  • Human Pose and Action Recognition
  • Emotion and Mood Recognition
  • Advanced Neural Network Applications

Sicheng Zhao has published within a broad range of prominent venues. The most frequent publication outlets include:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • IEEE Transactions on Multimedia
  • ACM Transactions on Multimedia Computing Communications and Applications
  • IEEE Transactions on Image Processing

Notable recent papers include:

  • "A Review of Single-Source Deep Unsupervised Visual Domain Adaptation" (2020), IEEE Transactions on Neural Networks and Learning Systems
  • "Multi-Source Distilling Domain Adaptation" (2020), Proceedings of the AAAI Conference on Artificial Intelligence
  • "Multimodal Sentiment Analysis With Image-Text Interaction Network" (2022), IEEE Transactions on Multimedia
  • "Personality-Assisted Multi-Task Learning for Generic and Personalized Image Aesthetics Assessment" (2020), IEEE Transactions on Image Processing
  • "Affective Image Content Analysis: Two Decades Review and New Perspectives" (2021), IEEE Transactions on Pattern Analysis and Machine Intelligence

Their frequent collaborators include:

  • Guiguang Ding
  • Kurt Keutzer
  • Jufeng Yang
  • Jungong Han
  • Xiangyu Yue

Sicheng Zhao's publications are primarily situated within computer science, with key contributions to specialized subfields such as experimental and cognitive psychology and computer networks and communications. Their work addresses both foundational research and applied aspects of image and video processing, multimodal systems, and neural network technologies.

Best Publications

  • Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

    Spyridon Bakas;Mauricio Reyes;Andras Jakab;Stefan Bauer

  • Auto-encoder based dimensionality reduction

    Yasi Wang;Hongxun Yao;Sicheng Zhao

  • SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud

    Bichen Wu;Xuanyu Zhou;Sicheng Zhao;Xiangyu Yue

  • Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions

    Bichen Wu;Alvin Wan;Xiangyu Yue;Peter Jin

  • Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization Without Accessing Target Domain Data

    Xiangyu Yue;Yang Zhang;Sicheng Zhao;Alberto Sangiovanni-Vincentelli

  • Exploring Principles-of-Art Features For Image Emotion Recognition

    Sicheng Zhao;Yue Gao;Xiaolei Jiang;Hongxun Yao

  • A Review of Single-Source Deep Unsupervised Visual Domain Adaptation.

    Sicheng Zhao;Xiangyu Yue;Shanghang Zhang;Bo Li

  • SqueezeNext: Hardware-Aware Neural Network Design

    Amir Gholami;Kiseok Kwon;Bichen Wu;Zizheng Tai

  • Emotion Recognition From Multiple Modalities: Fundamentals and methodologies

    Sicheng Zhao;Guoli Jia;Jufeng Yang;Guiguang Ding

  • Multi-source Distilling Domain Adaptation

    Sicheng Zhao;Guangzhi Wang;Shanghang Zhang;Yang Gu

  • Continuous Probability Distribution Prediction of Image Emotions via Multitask Shared Sparse Regression

    Sicheng Zhao;Hongxun Yao;Yue Gao;Rongrong Ji

  • Predicting Personalized Image Emotion Perceptions in Social Networks

    Sicheng Zhao;Hongxun Yao;Yue Gao;Guiguang Ding

  • Affective Image Content Analysis: Two Decades Review and New Perspectives.

    Sicheng Zhao;Xingxu Yao;Jufeng Yang;Guoli Jia

  • Personality-Assisted Multi-Task Learning for Generic and Personalized Image Aesthetics Assessment

    Leida Li;Hancheng Zhu;Sicheng Zhao;Guiguang Ding

  • Multi-source Domain Adaptation for Semantic Segmentation

    Sicheng Zhao;Bo Li;Xiangyu Yue;Yang Gu

  • A Neural Multi-Task Learning Framework to Jointly Model Medical Named Entity Recognition and Normalization

    Sendong Zhao;Ting Liu;Sicheng Zhao;Fei Wang

  • Affective Image Retrieval via Multi-Graph Learning

    Sicheng Zhao;Hongxun Yao;You Yang;Yanhao Zhang

  • Real-Time Multimedia Social Event Detection in Microblog

    Sicheng Zhao;Yue Gao;Guiguang Ding;Tat-Seng Chua

  • PDANet: Polarity-consistent Deep Attention Network for Fine-grained Visual Emotion Regression

    Sicheng Zhao;Zizhou Jia;Hui Chen;Leida Li

  • Predicting Personalized Emotion Perceptions of Social Images

    Sicheng Zhao;Hongxun Yao;Yue Gao;Rongrong Ji

  • Multi-source Domain Adaptation in the Deep Learning Era: A Systematic Survey.

    Sicheng Zhao;Bo Li;Colorado Reed;Pengfei Xu

Frequent Co-Authors

Hongxun Yao
Hongxun Yao Harbin Institute of Technology
Kurt Keutzer
Kurt Keutzer University of California, Berkeley
Guiguang Ding
Guiguang Ding Tsinghua University
Jungong Han
Jungong Han Aberystwyth University
Yue Gao
Yue Gao Tsinghua University
Tat-Seng Chua
Tat-Seng Chua National University of Singapore
Qingming Huang
Qingming Huang University of Chinese Academy of Sciences
Alberto Sangiovanni-Vincentelli
Alberto Sangiovanni-Vincentelli University of California, Berkeley
Björn Schuller
Björn Schuller Imperial College London
Shanghang Zhang
Shanghang Zhang Carnegie Mellon University

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

As you explore options for studying Computer Science in the USA, consider how diverse online degrees can complement your goals or offer new career pathways. The digital landscape enables you to study related fields conveniently and affordably from anywhere.

If you’re interested in combining technology with law enforcement or legal studies, a cheap criminal justice degree could be a cost-effective way to start a career in cybersecurity, forensics, or crime analysis.

For those drawn to the financial sector, pursuing an online accounting degree cost offers insights into data analysis, auditing, and business technology—valuable skills in both accounting and computer science careers.

Looking to advance your expertise? The best data science masters programs online allow professionals to specialize in big data, artificial intelligence, and analytics—fields highly sought after in today’s tech industry.

If your interests intersect with project management and technology, explore the fastest online construction management degree programs. These can quickly prepare you for roles integrating software, data, and management in the construction sector.

Exploring related online degrees can diversify your skill set, expand career opportunities, and adapt your education to fit your interests and schedule.

Best Scientists Citing Sicheng Zhao

Trending Scientists

Recently Published Articles