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Alexander M. Rush

Alexander M. Rush

Cornell University
United States

D-Index & Metrics

Discipline name D-Index World Ranking Current World Ranking National Ranking Current National Ranking Publications Citations
Computer Science 64 2533 2454 1265 1217 148 24490

Alexander M. Rush publications per year

The chart shows the history of publications by Alexander M. Rush between 2005 and 2026, highlighting the no. of papers published in each year and offering an overview of the publication velocity of this scholar. Alexander M. Rush published across 22 years, from 2005 to 2026, averaging 9.1 papers a year. Output peaked at 24 publications in 2021. 8 of the 201 publications appeared in the last two years.

No. of publications
5 10 15 20
Bar chart. Horizontal axis: year, 2005 to 2026. Vertical axis: number of publications, 0 to 24. Peak 24 publications in 2021. 2005: 1 publication 2006: 1 publication 2007: 0 publications 2008: 2 publications 2009: 0 publications 2010: 2 publications 2011: 3 publications 2012: 4 publications 2013: 3 publications 2014: 2 publications 2015: 7 publications 2016: 18 publications 2017: 9 publications 2018: 17 publications 2019: 22 publications 2020: 21 publications 2021: 24 publications 2022: 17 publications 2023: 24 publications 2024: 16 publications 2025: 7 publications 2026: 1 publication
2005 2026

201 publications in total across all disciplines

View publications per year as a table
Alexander M. Rush: publications per year, 2005 to 2026
Year Publications
2005 1
2006 1
2007 0
2008 2
2009 0
2010 2
2011 3
2012 4
2013 3
2014 2
2015 7
2016 18
2017 9
2018 17
2019 22
2020 21
2021 24
2022 17
2023 24
2024 16
2025 7
2026 1
Total 201
Download as CSV

Alexander M. Rush 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 Alexander M. Rush 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, 142–151 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: 148 publications — 26th percentile

26% 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
142–151 609 148
152–161 559
162–171 534
172–181 556
182–191 583
192–201 519
202–211 508
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

Alexander M. Rush 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 Alexander M. Rush 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, 64–65 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: 64 D-Index — 82nd percentile

82% 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
60–61 337
62–63 308
64–65 292 64
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
  • Programming language
  • Machine learning

His primary areas of investigation include Artificial intelligence, Natural language processing, Machine learning, Machine translation and Automatic summarization. His research in Word, Recurrent neural network, Parsing, Question answering and Feature learning are components of Artificial intelligence. His Natural language processing research includes elements of Speech recognition and Transformer.

His research in the fields of Artificial neural network and Autoencoder overlaps with other disciplines such as Key and Discretization. As a part of the same scientific study, Alexander M. Rush usually deals with the Machine translation, concentrating on Programming language and frequently concerns with Feature, Translation, CUDA and Deep learning. In his research, Attention model and Training set is intimately related to Sentence, which falls under the overarching field of Automatic summarization.

His most cited work include:

  • A Neural Attention Model for Abstractive Sentence Summarization (1249 citations)
  • Character-aware neural language models (1033 citations)
  • OpenNMT: Open-Source Toolkit for Neural Machine Translation (1007 citations)

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

Alexander M. Rush spends much of his time researching Artificial intelligence, Natural language processing, Machine learning, Language model and Inference. Sentence, Deep learning, Parsing, Automatic summarization and Word are the primary areas of interest in his Artificial intelligence study. His Automatic summarization study combines topics in areas such as Domain, Paraphrase, Attention model and Training set.

His Natural language processing research is multidisciplinary, incorporating elements of Recurrent neural network, Simple, Speech recognition and Coreference. His research in Machine learning intersects with topics in Generative grammar, State, Natural language and Machine translation. His Inference study integrates concerns from other disciplines, such as Latent variable, Question answering, Graphical model, Structure and Pattern recognition.

He most often published in these fields:

  • Artificial intelligence (74.36%)
  • Natural language processing (31.41%)
  • Machine learning (28.85%)

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

  • Artificial intelligence (74.36%)
  • Machine learning (28.85%)
  • Transformer (7.69%)

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

Alexander M. Rush mostly deals with Artificial intelligence, Machine learning, Transformer, Natural language processing and Deep learning. His studies in Artificial intelligence integrate themes in fields like Structure and Simple. His Leverage, Product of experts and Value study in the realm of Machine learning interacts with subjects such as Training.

The study incorporates disciplines such as Algorithm and Machine translation in addition to Transformer. His research integrates issues of Document retrieval, Similarity and Rank in his study of Natural language processing. The concepts of his Inference study are interwoven with issues in Language model and Relaxation.

Between 2019 and 2021, his most popular works were:

  • Transformers: State-of-the-Art Natural Language Processing (314 citations)
  • Movement Pruning: Adaptive Sparsity by Fine-Tuning (20 citations)
  • Visual Interaction with Deep Learning Models through Collaborative Semantic Inference (20 citations)

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

  • Artificial intelligence
  • Programming language
  • Machine learning

Alexander M. Rush mainly focuses on Artificial intelligence, Deep learning, Algorithm, Transformer and Simple. His studies deal with areas such as Machine learning, Human–computer interaction and Natural language processing as well as Artificial intelligence. In general Natural language processing, his work in Noun and Syntax is often linked to Concreteness linking many areas of study.

The Deep learning study which covers Inference that intersects with Floating point, Quantization, Artificial neural network and Interaction design. His work deals with themes such as Language model, Supervised learning and Transfer of learning, which intersect with Algorithm. The study incorporates disciplines such as Decoding methods, Markov chain and Machine translation in addition to Transformer.

Best Publications

  • A Neural Attention Model for Abstractive Sentence Summarization

    Alexander M. Rush;Sumit Chopra;Jason Weston

  • Character-aware neural language models

    Yoon Kim;Yacine Jernite;David Sontag;Alexander M. Rush

  • OpenNMT: Open-Source Toolkit for Neural Machine Translation

    Guillaume Klein;Yoon Kim;Yuntian Deng;Jean Senellart

  • Transformers: State-of-the-Art Natural Language Processing

    Thomas Wolf;Lysandre Debut;Victor Sanh;Julien Chaumond

  • BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

    Unknown

  • Abstractive Sentence Summarization with Attentive Recurrent Neural Networks

    Sumit Chopra;Michael Auli;Alexander M. Rush

  • Sequence-Level Knowledge Distillation

    Yoon Kim;Alexander M. Rush

  • Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks

    Jason Weston;Antoine Bordes;Sumit Chopra;Alexander M. Rush

  • Bottom-Up Abstractive Summarization

    Sebastian Gehrmann;Yuntian Deng;Alexander M. Rush

  • Multitask Prompted Training Enables Zero-Shot Task Generalization

    Victor Sanh;Albert Webson;Colin Raffel;Stephen H. Bach

  • Challenges in Data-to-Document Generation

    Sam Joshua Wiseman;Stuart Merrill Shieber;Alexander Sasha Matthew Rush

  • Sequence-to-Sequence Learning as Beam-Search Optimization

    Sam Wiseman;Alexander M. Rush

  • LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks

    Hendrik Strobelt;Sebastian Gehrmann;Hanspeter Pfister;Alexander M. Rush

  • Structured Attention Networks

    Yoon Kim;Carl Denton;Luong Hoang;Alexander M. Rush

  • PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts

    Unknown

  • Datasets: A Community Library for Natural Language Processing

    Quentin Lhoest;Albert Villanova del Moral;Yacine Jernite;Abhishek Thakur

  • Commonsense Knowledge Mining from Pretrained Models

    Joe Davison;Joshua Feldman;Alexander M. Rush

  • On Dual Decomposition and Linear Programming Relaxations for Natural Language Processing

    Alexander M Rush;David Sontag;Michael Collins;Tommi Jaakkola

  • GLTR: Statistical Detection and Visualization of Generated Text

    Sebastian Gehrmann;Hendrik Strobelt;Alexander M. Rush

  • Adversarially Regularized Autoencoders

    Junbo Jake Zhao;Junbo Jake Zhao;Yoon Kim;Kelly Zhang;Alexander M. Rush

  • How Many Data Points is a Prompt Worth

    Teven Le Scao;Alexander M. Rush

  • Commonsense Knowledge Mining from Pretrained Models

    Joshua Feldman;Joe Davison;Alexander M. Rush

  • Movement Pruning: Adaptive Sparsity by Fine-Tuning

    Victor Sanh;Thomas Wolf;Alexander M. Rush

Frequent Co-Authors

Stuart M. Shieber
Stuart M. Shieber Harvard University
Hendrik Strobelt
Hendrik Strobelt IBM (United States)
David Brooks
David Brooks Harvard University
Hanspeter Pfister
Hanspeter Pfister Harvard University
Michael Collins
Michael Collins Google (United States)
Sumit Chopra
Sumit Chopra New York University
Jason Weston
Jason Weston Facebook (United States)
David Sontag
David Sontag MIT
Gu-Yeon Wei
Gu-Yeon Wei Harvard University
Claire Cardie
Claire Cardie Cornell University

External Links

Personal website of Alexander M. Rush Google Scholar page

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Mirella Lapata

University of Edinburgh

Publications: 68

Graham Neubig

Graham Neubig

Carnegie Mellon University

Publications: 63

Noah A. Smith

Noah A. Smith

University of Washington

Publications: 57

Kyunghyun Cho

Kyunghyun Cho

New York University

Publications: 56

André F. T. Martins

André F. T. Martins

Instituto Superior Técnico

Publications: 53

Mohit Bansal

Mohit Bansal

University of North Carolina at Chapel Hill

Publications: 50

Hinrich Schütze

Hinrich Schütze

Ludwig-Maximilians-Universität München

Publications: 46

Yejin Choi

Yejin Choi

Stanford University

Publications: 43

Yue Zhang

Yue Zhang

Westlake University

Publications: 43

Andy Way

Andy Way

Dublin City University

Publications: 41

Kevin Gimpel

Kevin Gimpel

Toyota Technological Institute at Chicago

Publications: 40

Xuanjing Huang

Xuanjing Huang

Fudan University

Publications: 39

Furu Wei

Furu Wei

Microsoft (United States)

Publications: 39

Chris Dyer

Chris Dyer

Google (United States)

Publications: 35

Tommi S. Jaakkola

Tommi S. Jaakkola

MIT

Publications: 35

Tie-Yan Liu

Tie-Yan Liu

Microsoft (United States)

Publications: 35

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