World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
6539
World Ranking
12472
National Ranking
481

Lili Mou 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 Lili Mou 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: 96 publications — 7th percentile

7% of scientists in this discipline score the same or lower.

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

Lili Mou 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 Lili Mou 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: 33 D-Index — 13th percentile

13% of scientists in this discipline score the same or lower.

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

Overview

Lili Mou is affiliated with the University of Waterloo in Canada and specializes in computer science, with a particular focus on artificial intelligence. Their scholarly work encompasses 109 publications primarily in the field of computer science, with 90 of those specifically in artificial intelligence. Other subfields of study include computer vision and pattern recognition, information systems, signal processing, and cognitive neuroscience.

The research topics covered by Lili Mou's publications span several areas:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Advanced Text Analysis Techniques
  • Multimodal Machine Learning Applications
  • Machine Learning and Data Classification
  • Software Engineering Research
  • Music and Audio Processing

The scientist has contributed to numerous papers with various coauthors, frequently collaborating with:

  • Yanshuai Cao
  • Yuqiao Wen
  • Chenyang Huang
  • Behzad Shayegh
  • Osmar R. Zai͏̈ane

Lili Mou's research has been published in multiple venues, with a substantial number of papers appearing in:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Neurocomputing
  • Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  • Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment

Selected recent papers illustrate the range and focus of their work:

  • TreeGen: A Tree-Based Transformer Architecture for Code Generation, 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • Unsupervised Text Generation by Learning from Search, 2020, arXiv (Cornell University)
  • Simulated annealing for optimization of graphs and sequences, 2021, Neurocomputing
  • Document-Level Relation Extraction with Sentences Importance Estimation and Focusing, 2022, Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  • Non-Autoregressive Translation with Layer-Wise Prediction and Deep Supervision, 2021, arXiv (Cornell University)

Best Publications

  • Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths

    Yan Xu;Lili Mou;Ge Li;Yunchuan Chen

  • Convolutional neural networks over tree structures for programming language processing

    Lili Mou;Ge Li;Lu Zhang;Tao Wang

  • Distilling Task-Specific Knowledge from BERT into Simple Neural Networks

    Raphael Tang;Yao Lu;Linqing Liu;Lili Mou

  • Natural Language Inference by Tree-Based Convolution and Heuristic Matching

    Lili Mou;Rui Men;Ge Li;Yan Xu

  • How Transferable are Neural Networks in NLP Applications

    Lili Mou;Zhao Meng;Rui Yan;Ge Li

  • Disentangled Representation Learning for Non-Parallel Text Style Transfer

    Vineet John;Lili Mou;Hareesh Bahuleyan;Olga Vechtomova

  • Improved Relation Classification by Deep Recurrent Neural Networks with Data Augmentation

    Yan Xu;Ran Jia;Lili Mou;Ge Li

  • Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation

    Lili Mou;Yiping Song;Rui Yan;Ge Li

  • RUBER: An Unsupervised Method for Automatic Evaluation of Open-Domain Dialog Systems

    Chongyang Tao;Lili Mou;Dongyan Zhao;Rui Yan

  • Affective Neural Response Generation

    Nabiha Asghar;Pascal Poupart;Jesse Hoey;Xin Jiang

  • TreeGen: A Tree-Based Transformer Architecture for Code Generation

    Zeyu Sun;Qihao Zhu;Yingfei Xiong;Yican Sun

  • CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling

    Ning Miao;Hao Zhou;Lili Mou;Rui Yan

  • Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Path

    Xu Yan;Lili Mou;Ge Li;Yunchuan Chen

  • Building Program Vector Representations for Deep Learning

    Hao Peng;Lili Mou;Ge Li;Yuxuan Liu

  • Discriminative Neural Sentence Modeling by Tree-Based Convolution

    Lili Mou;Hao Peng;Ge Li;Yan Xu

  • How to Make Context More Useful? An Empirical Study on Context-Aware Neural Conversational Models

    Zhiliang Tian;Rui Yan;Lili Mou;Yiping Song

  • A Grammar-Based Structural CNN Decoder for Code Generation

    Zeyu Sun;Qihao Zhu;Lili Mou;Yingfei Xiong

  • Order-Planning Neural Text Generation From Structured Data

    Lei Sha;Lili Mou;Tianyu Liu;Pascal Poupart

  • Generating Sentences from Disentangled Syntactic and Semantic Spaces.

    Yu Bao;Hao Zhou;Shujian Huang;Lei Li

  • Building Program Vector Representations for Deep Learning

    Lili Mou;Ge Li;Yuxuan Liu;Hao Peng

  • TBCNN: A Tree-Based Convolutional Neural Network for Programming Language Processing.

    Lili Mou;Ge Li;Zhi Jin;Lu Zhang

Frequent Co-Authors

Lu Zhang
Lu Zhang Peking University
Rui Yan
Rui Yan Renmin University of China
Pascal Poupart
Pascal Poupart University of Waterloo
Zhengdong Lu
Zhengdong Lu Huawei Technologies (China)
Yingfei Xiong
Yingfei Xiong Peking University
Shujian Huang
Shujian Huang Nanjing University
Dongyan Zhao
Dongyan Zhao Peking University
Hang Li
Hang Li ByteDance
Zhi Jin
Zhi Jin Wuhan University
Tao Wang
Tao Wang Stanford University

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