World's Best Scientists 2026 revealed!
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Caglar Gulcehre
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Rising Stars
2025

D-Index & Metrics

Discipline name D-Index World Ranking Current World Ranking National Ranking Current National Ranking Publications Citations
Rising Stars 38 708 708 38 38 60 58051
Computer Science 38 9917 9635 618 610 59 62545

Caglar Gulcehre publications per year

The chart shows the history of publications by Caglar Gulcehre between 2013 and 2025, highlighting the no. of papers published in each year and offering an overview of the publication velocity of this scholar. Caglar Gulcehre published across 13 years, from 2013 to 2025, averaging 7.3 papers a year. Output peaked at 17 publications in 2024. 29 of the 95 publications appeared in the last two years.

No. of publications
5 10 15
Bar chart. Horizontal axis: year, 2013 to 2025. Vertical axis: number of publications, 0 to 17. Peak 17 publications in 2024. 2013: 2 publications 2014: 7 publications 2015: 2 publications 2016: 10 publications 2017: 11 publications 2018: 5 publications 2019: 5 publications 2020: 13 publications 2021: 5 publications 2022: 1 publication 2023: 5 publications 2024: 17 publications 2025: 12 publications
2013 2025

95 publications in total across all disciplines

View publications per year as a table
Caglar Gulcehre: publications per year, 2013 to 2025
Year Publications
2013 2
2014 7
2015 2
2016 10
2017 11
2018 5
2019 5
2020 13
2021 5
2022 1
2023 5
2024 17
2025 12
Total 95
Download as CSV

Caglar Gulcehre 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 Caglar Gulcehre 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, 52–61 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: 59 publications — 1st percentile

1% 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 59
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
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

Caglar Gulcehre 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 Caglar Gulcehre 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, 38–39 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: 38 D-Index — 30th percentile

30% 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 38
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
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

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Caglar Gulcehre is a researcher affiliated with DeepMind in the United Kingdom. Their research contributions primarily lie within the field of Computer Science, with a focus on areas such as Artificial Intelligence, Computer Vision and Pattern Recognition, Management Science and Operations Research, Computer Networks and Communications, and Control and Systems Engineering.

The scientist's work covers several main topics, including:

  • Reinforcement Learning in Robotics
  • Machine Learning and Algorithms
  • Topic Modeling
  • Natural Language Processing Techniques
  • Advanced Bandit Algorithms Research
  • Machine Learning and Data Classification
  • Multimodal Machine Learning Applications

Caglar Gulcehre has authored numerous publications, predominantly appearing in the venue arXiv (Cornell University), with a total of 39 publications in this outlet. Some recent papers include:

  • Understanding the Impact of Value Selection Heuristics in Scheduling Problems, 2025, arXiv (Cornell University)
  • Static Analysis of Shape in TensorFlow Programs, 2020, arXiv (Cornell University)
  • Critic Regularized Regression, 2020, arXiv (Cornell University)
  • Acme: A Research Framework for Distributed Reinforcement Learning, 2020, arXiv (Cornell University)
  • Assessing Factoid Question-Answer Generation for Portuguese (Short Paper), 2020, arXiv (Cornell University)

The number of citations for some papers highlights their impact within the research community, such as 2380 citations for work on value selection heuristics and 1669 for static analysis in TensorFlow programs.

Frequent collaborators in their work include Razvan Pascanu, Nando de Freitas, Konrad Żołna, Tom Le Paine, and Ziyu Wang, with collaboration counts ranging from 5 to 10 joint publications.

Best Publications

  • Learning Phrase Representations using RNN Encoder--Decoder for Statistical Machine Translation

    Kyunghyun Cho;Bart van Merrienboer;Caglar Gulcehre;Dzmitry Bahdanau

  • Empirical evaluation of gated recurrent neural networks on sequence modeling

    Junyoung Chung;Çaglar Gülçehre;KyungHyun Cho;Yoshua Bengio;Yoshua Bengio;Yoshua Bengio

  • Grandmaster level in StarCraft II using multi-agent reinforcement learning.

    Oriol Vinyals;Igor Babuschkin;Wojciech M. Czarnecki;Michaël Mathieu

  • Relational inductive biases, deep learning, and graph networks

    Peter W. Battaglia;Jessica B. Hamrick;Victor Bapst;Alvaro Sanchez-Gonzalez

  • Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond

    Ramesh Nallapati;Bowen Zhou;Cicero Nogueira dos santos;Caglar Gulcehre

  • Theano: A Python framework for fast computation of mathematical expressions

    Rami Al-Rfou;Guillaume Alain;Amjad Almahairi

  • Identifying and attacking the saddle point problem in high-dimensional non-convex optimization

    Yann N Dauphin;Razvan Pascanu;Caglar Gulcehre;Kyunghyun Cho

  • How to Construct Deep Recurrent Neural Networks

    Razvan Pascanu;Caglar Gulcehre;Kyunghyun Cho;Yoshua Bengio

  • Gated Feedback Recurrent Neural Networks

    Junyoung Chung;Caglar Gulcehre;Kyunghyun Cho;Yoshua Bengio;Yoshua Bengio

  • On using monolingual corpora in neural machine translation

    Çaglar Gülçehre;Orhan Firat;Kelvin Xu;Kyunghyun Cho

  • Pointing the unknown words

    Caglar Gulcehre;Sungjin Ahn;Ramesh Nallapati;Bowen Zhou

  • EmoNets: Multimodal deep learning approaches for emotion recognition in video

    Samira Ebrahimi Kahou;Xavier Bouthillier;Pascal Lamblin;Çaglar Gülçehre

  • Combining modality specific deep neural networks for emotion recognition in video

    Samira Ebrahimi Kahou;Christopher Pal;Xavier Bouthillier;Pierre Froumenty

  • Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus

    Iulian Vlad Serban;Alberto García-Durán;Çaglar Gülçehre;Sungjin Ahn

  • Recurrent Batch Normalization

    Tim Cooijmans;Nicolas Ballas;César Laurent;Çaglar Gülçehre

  • Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning

    Natasha Jaques;Angeliki Lazaridou;Edward Hughes;Çaglar Gülçehre

  • Noisy activation functions

    Caglar Gulcehre;Marcin Moczulski;Misha Denil;Yoshua Bengio

  • Learned-norm pooling for deep feedforward and recurrent neural networks

    Caglar Gulcehre;Kyunghyun Cho;Razvan Pascanu;Yoshua Bengio

  • Stabilizing Transformers for Reinforcement Learning

    Emilio Parisotto;Francis Song;Jack Rae;Razvan Pascanu

  • Machine Comprehension by Text-to-Text Neural Question Generation

    Xingdi Yuan;Tong Wang;Caglar Gulcehre;Alessandro Sordoni

  • Critic Regularized Regression

    Ziyu Wang;Alexander Novikov;Konrad Zolna;Jost Tobias Springenberg

  • Acme: A Research Framework for Distributed Reinforcement Learning

    Matt Hoffman;Bobak Shahriari;John Aslanides;Gabriel Barth-Maron

  • Machine Comprehension by Text-to-Text Neural Question Generation

    Xingdi Yuan;Tong Wang;Caglar Gulcehre;Alessandro Sordoni

Frequent Co-Authors

Yoshua Bengio
Yoshua Bengio University of Montreal
Nando de Freitas
Nando de Freitas DeepMind (United Kingdom)
Razvan Pascanu
Razvan Pascanu DeepMind (United Kingdom)
Kyunghyun Cho
Kyunghyun Cho New York University
Nicolas Heess
Nicolas Heess DeepMind (United Kingdom)
Aaron Courville
Aaron Courville University of Montreal
Adam Trischler
Adam Trischler Microsoft (United States)
Yann N. Dauphin
Yann N. Dauphin Google (United States)
Bowen Zhou
Bowen Zhou IBM (United States)
Raia Hadsell
Raia Hadsell DeepMind (United Kingdom)

External Links

Personal website of Caglar Gulcehre Google Scholar page

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