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Ying Nian Wu

D-Index & Metrics

Discipline name D-Index World Ranking Current World Ranking National Ranking Current National Ranking Publications Citations
Computer Science 59 3378 3282 1635 1576 206 16849

Ying Nian Wu publications per year

The chart shows the history of publications by Ying Nian Wu between 1993 and 2021, highlighting the no. of papers published in each year and offering an overview of the publication velocity of this scholar. Ying Nian Wu published across 29 years, from 1993 to 2021, averaging 6.9 papers a year. Output peaked at 31 publications in 2021. 56 of the 200 publications appeared in the last two years.

No. of publications
10 20 30
Bar chart. Horizontal axis: year, 1993 to 2021. Vertical axis: number of publications, 0 to 31. Peak 31 publications in 2021. 1993: 1 publication 1994: 0 publications 1995: 0 publications 1996: 1 publication 1997: 3 publications 1998: 3 publications 1999: 4 publications 2000: 4 publications 2001: 5 publications 2002: 2 publications 2003: 3 publications 2004: 3 publications 2005: 2 publications 2006: 2 publications 2007: 7 publications 2008: 1 publication 2009: 1 publication 2010: 3 publications 2011: 6 publications 2012: 0 publications 2013: 2 publications 2014: 10 publications 2015: 4 publications 2016: 15 publications 2017: 12 publications 2018: 22 publications 2019: 28 publications 2020: 25 publications 2021: 31 publications
1993 2021

200 publications in total across all disciplines

View publications per year as a table
Ying Nian Wu: publications per year, 1993 to 2021
Year Publications
1993 1
1994 0
1995 0
1996 1
1997 3
1998 3
1999 4
2000 4
2001 5
2002 2
2003 3
2004 3
2005 2
2006 2
2007 7
2008 1
2009 1
2010 3
2011 6
2012 0
2013 2
2014 10
2015 4
2016 15
2017 12
2018 22
2019 28
2020 25
2021 31
Total 200
Download as CSV

Ying Nian Wu 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 Ying Nian Wu sits on this spectrum.

No. of scientists
200 400 600
Bar chart with 97 bars. Horizontal axis: publications, 32–41 to 991+. Vertical axis: number of scientists, 0 to 609. Most scientists, 609, have 142–151 publications. The last bar groups every scientist with 991 publications or more. The highlighted bar, 202–211 publications, is where this scientist sits. 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
32–41 publications 991+

This scientist: 206 publications — 48th percentile

48% of scientists in this discipline score the same or lower.

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

View publications distribution as a table
Number of Computer Science scientists by publication count, Research.com 2026 ranking edition. Based on 14,188 ranked scientists.
Publications Scientists This scientist
32–41 7
42–51 22
52–61 82
62–71 134
72–81 249
82–91 324
92–101 421
102–111 420
112–121 497
122–131 544
132–141 555
142–151 609
152–161 559
162–171 534
172–181 556
182–191 583
192–201 519
202–211 508 206
212–221 490
222–231 437
232–241 423
242–251 408
252–261 377
262–271 301
272–281 335
282–291 320
292–301 293
302–311 250
312–321 238
322–331 206
332–341 209
342–351 208
352–361 162
362–371 176
372–381 127
382–391 158
392–401 128
402–411 104
412–421 94
422–431 99
432–441 83
442–451 108
452–461 73
462–471 77
472–481 69
482–491 84
492–501 62
502–511 54
512–521 57
522–531 51
532–541 51
542–551 32
552–561 38
562–571 28
572–581 43
582–591 33
592–601 41
602–611 32
612–621 28
622–631 25
632–641 27
642–651 17
652–661 20
662–671 17
672–681 15
682–691 14
692–701 21
702–711 13
712–721 12
722–731 19
732–741 14
742–751 12
752–761 10
762–771 10
772–781 11
782–791 10
792–801 11
802–811 8
812–821 8
822–831 7
832–841 11
842–851 10
852–861 5
862–871 9
872–881 4
882–891 6
892–901 3
902–911 6
912–921 3
922–931 2
932–941 2
942–951 2
952–961 3
962–971 3
972–981 3
982–990 5
991+ 100
Download as CSV

Ying Nian Wu 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 Ying Nian Wu sits on this spectrum.

No. of scientists
200 400 600 800
Bar chart with 52 bars. Horizontal axis: D-Index, 30–31 to 131+. Vertical axis: number of scientists, 0 to 990. Most scientists, 990, have 36–37 D-Index. The last bar groups every scientist with 131 D-Index or more. The highlighted bar, 58–59 D-Index, is where this scientist sits. 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
30–31 D-Index 131+

This scientist: 59 D-Index — 77th percentile

77% of scientists in this discipline score the same or lower.

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

View D-Index distribution as a table
Number of Computer Science scientists by D-index, Research.com 2026 ranking edition. Based on 14,188 ranked scientists.
D-Index Scientists This scientist
30–31 879
32–33 983
34–35 918
36–37 990
38–39 968
40–41 907
42–43 821
44–45 763
46–47 689
48–49 543
50–51 543
52–53 518
54–55 500
56–57 458
58–59 400 59
60–61 337
62–63 308
64–65 292
66–67 249
68–69 213
70–71 192
72–73 189
74–75 165
76–77 139
78–79 119
80–81 121
82–83 113
84–85 88
86–87 87
88–89 75
90–91 69
92–93 57
94–95 46
96–97 38
98–99 34
100–101 36
102–103 27
104–105 37
106–107 18
108–109 31
110–111 19
112–113 16
114–115 12
116–117 20
118–119 15
120–121 5
122–123 20
124–125 8
126–127 5
128–129 7
130 3
131+ 98
Download as CSV

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Machine learning

Artificial intelligence, Algorithm, Image texture, Pattern recognition and Computer vision are his primary areas of study. His Artificial intelligence study frequently draws connections to other fields, such as Random field. The study incorporates disciplines such as Gibbs sampling, Probabilistic logic, Bioinformatics and Expectation–maximization algorithm in addition to Algorithm.

His Gibbs sampling study integrates concerns from other disciplines, such as Probability distribution and Markov chain Monte Carlo. The various areas that Ying Nian Wu examines in his Pattern recognition study include Convolution and Autoencoder. In general Computer vision study, his work on Image processing, Texture filtering and Texture compression often relates to the realm of Basis, thereby connecting several areas of interest.

His most cited work include:

  • A high-resolution map of active promoters in the human genome (830 citations)
  • Dynamic Textures (820 citations)
  • rMATS: Robust and flexible detection of differential alternative splicing from replicate RNA-Seq data (729 citations)

What are the main themes of his work throughout his whole career to date?

His primary scientific interests are in Artificial intelligence, Pattern recognition, Algorithm, Convolutional neural network and Markov chain Monte Carlo. His studies deal with areas such as Machine learning and Computer vision as well as Artificial intelligence. His Pattern recognition research integrates issues from Object, Statistical model and Random field.

The study incorporates disciplines such as Markov random field and Principle of maximum entropy in addition to Random field. His Algorithm research incorporates themes from Inference, Maximum likelihood, Generator, Markov chain and Function. The concepts of his Convolutional neural network study are interwoven with issues in Question answering, Visualization, Training set and Benchmark.

He most often published in these fields:

  • Artificial intelligence (61.21%)
  • Pattern recognition (34.11%)
  • Algorithm (22.90%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (61.21%)
  • Algorithm (22.90%)
  • Function (7.94%)

In recent papers he was focusing on the following fields of study:

His primary areas of study are Artificial intelligence, Algorithm, Function, Markov chain Monte Carlo and Pattern recognition. His study on Artificial intelligence is mostly dedicated to connecting different topics, such as Energy. His work deals with themes such as Maximum likelihood, Generator, Prior probability and Divergence, which intersect with Algorithm.

Ying Nian Wu usually deals with Function and limits it to topics linked to Iterative method and Solver and Langevin dynamics. His biological study spans a wide range of topics, including Latent variable model, Latent variable, Sampling and Generative grammar, Generative model. His Pattern recognition research includes elements of Effective method, Graph Node, Graph and Benchmark.

Between 2019 and 2021, his most popular works were:

  • Cooperative Training of Descriptor and Generator Networks (56 citations)
  • Flow Contrastive Estimation of Energy-Based Models (25 citations)
  • Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense (17 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Statistics
  • Machine learning

The scientist’s investigation covers issues in Artificial intelligence, Energy, Function, Markov chain Monte Carlo and Algorithm. His Artificial intelligence study frequently links to other fields, such as Speech coding. His research integrates issues of Langevin dynamics, Iterative method, Anomaly detection and Markov process in his study of Function.

His Markov chain Monte Carlo research is multidisciplinary, relying on both Sampling and Machine learning, Convolutional neural network. His study in Machine learning is interdisciplinary in nature, drawing from both Space, Generative model and Code. His Algorithm study integrates concerns from other disciplines, such as Maximum likelihood and Flow.

Best Publications

  • rMATS: Robust and flexible detection of differential alternative splicing from replicate RNA-Seq data

    Shihao Shen;Juw Won Park;Zhi-xiang Lu;Lan Lin

  • Dynamic Textures

    Gianfranco Doretto;Alessandro Chiuso;Ying Nian Wu;Stefano Soatto

  • Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling

    Song Chun Zhu;Yingnian Wu;David Mumford

  • Interpretable Convolutional Neural Networks

    Quanshi Zhang;Ying Nian Wu;Song-Chun Zhu

  • Minimax Entropy Principle and Its Application to Texture Modeling

    Song Chun Zhu;Ying Nian Wu;David Mumford

  • Parameter expansion to accelerate EM: The PX-EM algorithm

    Chuanhai Liu;Donald B. Rubin;Ying Nian Wu

  • Parameter Expansion for Data Augmentation

    Jun S. Liu;Ying Nian Wu

  • Deep Learning With TensorFlow: A Review:

    Bo Pang;Erik Nijkamp;Ying Nian Wu

  • Multi-Agent Tensor Fusion for Contextual Trajectory Prediction

    Tianyang Zhao;Yifei Xu;Mathew Monfort;Wongun Choi

  • Dynamic texture recognition

    P. Saisan;G. Doretto;Ying Nian Wu;S. Soatto

  • Efficient Algorithms for Robust Estimation in Linear Mixed-Effects Models Using the Multivariate t Distribution

    José C Pinheiro;Chuanhai Liu;Ying Nian Wu

  • Dynamic textures

    S. Soatto;G. Doretto;Ying Nian Wu

  • Interpreting CNNs via Decision Trees

    Quanshi Zhang;Yu Yang;Haotian Ma;Ying Nian Wu

  • Interpreting CNN Knowledge via an Explanatory Graph

    Quanshi Zhang;Ruiming Cao;Feng Shi;Ying Nian Wu

  • Learning Active Basis Model for Object Detection and Recognition

    Ying Nian Wu;Zhangzhang Si;Haifeng Gong;Song-Chun Zhu

  • Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models

    Unknown

  • A theory of generative ConvNet

    Jianwen Xie;Yang Lu;Song-Chun Zhu;Ying Nian Wu

  • An expectation-maximization algorithm for probabilistic reconstructions of full-length isoforms from splice graphs

    Yi Xing;Tianwei Yu;Ying Nian Wu;Meenakshi Roy

  • Exploring texture ensembles by efficient Markov chain Monte Carlo-Toward a "trichromacy" theory of texture

    S.C. Zhu;X.W. Liu;Y.N. Wu

  • Non-negative matrix factorization of multimodal MRI, fMRI and phenotypic data reveals differential changes in default mode subnetworks in ADHD

    Ariana E. Anderson;Pamela K. Douglas;Wesley T. Kerr;Virginia S. Haynes

  • Primal sketch: Integrating structure and texture

    Cheng-en Guo;Song-Chun Zhu;Ying Nian Wu

  • What Are Textons

    Song Chun Zhu;Cheng-en Guo;Ying Nian Wu;Yizhou Wang

Frequent Co-Authors

Song-Chun Zhu
Song-Chun Zhu Peking University
Yi Xing
Yi Xing Children's Hospital of Philadelphia
Yixin Zhu
Yixin Zhu Peking University
Steven Shoptaw
Steven Shoptaw University of California, Los Angeles
Xiuwen Liu
Xiuwen Liu Florida State University
Jifeng Dai
Jifeng Dai Tsinghua University
Tianfu Wu
Tianfu Wu North Carolina State University
Yizhou Wang
Yizhou Wang Peking University
Wenguan Wang
Wenguan Wang Zhejiang University
Bing Ren
Bing Ren New York Genome Center

External Links

Personal website of Ying Nian Wu Google Scholar page

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Best Scientists Citing Ying Nian Wu

Song-Chun Zhu

Song-Chun Zhu

Peking University

Publications: 113

Alan L. Yuille

Alan L. Yuille

Johns Hopkins University

Publications: 62

Rama Chellappa

Rama Chellappa

Johns Hopkins University

Publications: 38

Yi Xing

Yi Xing

Children's Hospital of Philadelphia

Publications: 37

Pavan Turaga

Pavan Turaga

Arizona State University

Publications: 34

Nuno Vasconcelos

Nuno Vasconcelos

University of California, San Diego

Publications: 33

René Vidal

René Vidal

University of Pennsylvania

Publications: 33

Bing Ren

Bing Ren

New York Genome Center

Publications: 32

Liang Lin

Liang Lin

Sun Yat-sen University

Publications: 31

Xiuwen Liu

Xiuwen Liu

Florida State University

Publications: 30

Victor H. Lachos

Victor H. Lachos

University of Connecticut

Publications: 26

Antoni B. Chan

Antoni B. Chan

City University of Hong Kong

Publications: 26

Stefano Soatto

Stefano Soatto

University of California, Los Angeles

Publications: 25

Anuj Srivastava

Anuj Srivastava

Florida State University

Publications: 22

Fuchun Sun

Fuchun Sun

Tsinghua University

Publications: 21

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Eero P. Simoncelli

New York University

Publications: 20

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