World's Best Scientists 2026 revealed!
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Song Han

Song Han

MIT
United States

Award Badge
Rising Stars
2025

D-Index & Metrics

Discipline name D-Index World Ranking Current World Ranking National Ranking Current National Ranking Publications Citations
Rising Stars 53 236 236 35 35 119 44758
Computer Science 54 4418 4293 2063 1989 119 47121

Song Han publications per year

The chart shows the history of publications by Song Han between 2013 and 2025, highlighting the no. of papers published in each year and offering an overview of the publication velocity of this scholar. Song Han published across 13 years, from 2013 to 2025, averaging 13.5 papers a year. Output peaked at 26 publications in 2020. 30 of the 176 publications appeared in the last two years.

No. of publications
5 10 15 20 25
Bar chart. Horizontal axis: year, 2013 to 2025. Vertical axis: number of publications, 0 to 26. Peak 26 publications in 2020. 2013: 1 publication 2014: 0 publications 2015: 3 publications 2016: 19 publications 2017: 10 publications 2018: 15 publications 2019: 18 publications 2020: 26 publications 2021: 23 publications 2022: 11 publications 2023: 20 publications 2024: 13 publications 2025: 17 publications
2013 2025

176 publications in total across all disciplines

View publications per year as a table
Song Han: publications per year, 2013 to 2025
Year Publications
2013 1
2014 0
2015 3
2016 19
2017 10
2018 15
2019 18
2020 26
2021 23
2022 11
2023 20
2024 13
2025 17
Total 176
Download as CSV

Song Han 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 Song Han 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, 112–121 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: 119 publications — 14th percentile

14% 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
112–121 497 119
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

Song Han 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 Song Han 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, 54–55 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: 54 D-Index — 69th percentile

69% 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 54
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

Song Han is affiliated with MIT in the United States and has made contributions primarily in the fields of computer science and engineering. Their research focuses on areas including computer vision and pattern recognition, artificial intelligence, electrical and electronic engineering, aerospace engineering, and computer networks and communications.

The scientist's work encompasses multiple topics, such as advanced neural network applications, quantum computing algorithms and architecture, quantum information and cryptography, human pose and action recognition, adversarial robustness in machine learning, video surveillance and tracking methods, and domain adaptation and few-shot learning.

Song Han has published extensively, with a notable presence in venues such as arXiv (Cornell University), IEEE Transactions on Pattern Analysis and Machine Intelligence, Applied Intelligence, Proceedings of the 59th ACM/IEEE Design Automation Conference, and IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

  • Model Compression and Hardware Acceleration for Neural Networks: A Comprehensive Survey, 2020, Proceedings of the IEEE
  • MCUNet: Tiny Deep Learning on IoT Devices, 2020, arXiv (Cornell University)
  • Domain-specific hardware accelerators, 2020, Communications of the ACM
  • Lite Transformer with Long-Short Range Attention, 2020, arXiv (Cornell University)
  • Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications, 2022, ACM Transactions on Design Automation of Electronic Systems

Frequent coauthors of Song Han include Zhijian Liu, Ji Lin, Hanrui Wang, Yujun Lin, and Jiaqi Gu.

The range of Song Han's research venues and collaboration networks reflects involvement in diverse topics primarily related to neural networks, hardware acceleration, and deep learning on resource-constrained devices.

Best Publications

  • Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

    Song Han;Huizi Mao;William J. Dally;William J. Dally

  • SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

    Forrest N. Iandola;Song Han;Matthew W. Moskewicz;Khalid Ashraf

  • Learning both weights and connections for efficient neural networks

    Song Han;Jeff Pool;John Tran;William J. Dally

  • EIE: efficient inference engine on compressed deep neural network

    Song Han;Xingyu Liu;Huizi Mao;Jing Pu

  • TSM: Temporal Shift Module for Efficient Video Understanding

    Ji Lin;Chuang Gan;Song Han

  • ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware

    Han Cai;Ligeng Zhu;Song Han

  • AMC: AutoML for Model Compression and Acceleration on Mobile Devices

    Yihui He;Ji Lin;Zhijian Liu;Hanrui Wang

  • Deep Leakage from Gradients

    Ligeng Zhu;Zhijian Liu;Song Han

  • Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training

    Yujun Lin;Song Han;Huizi Mao;Yu Wang

  • HAQ: Hardware-Aware Automated Quantization With Mixed Precision

    Kuan Wang;Zhijian Liu;Yujun Lin;Ji Lin

  • Trained Ternary Quantization

    Chenzhuo Zhu;Song Han;Huizi Mao;William J. Dally

  • Model Compression and Hardware Acceleration for Neural Networks: A Comprehensive Survey

    Lei Deng;Guoqi Li;Song Han;Luping Shi

  • Once for All: Train One Network and Specialize it for Efficient Deployment

    Han Cai;Chuang Gan;Tianzhe Wang;Zhekai Zhang

  • ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA

    Song Han;Junlong Kang;Huizi Mao;Yiming Hu

  • Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution

    Haotian Tang;Zhijian Liu;Shengyu Zhao;Shengyu Zhao;Yujun Lin

  • Angel-Eye: A Complete Design Flow for Mapping CNN Onto Embedded FPGA

    Kaiyuan Guo;Lingzhi Sui;Jiantao Qiu;Jincheng Yu

  • Fast inference of deep neural networks in FPGAs for particle physics

    Javier Duarte;Edward Kreinar;Maurizio Pierini;Nhan Tran

  • Point-Voxel CNN for Efficient 3D Deep Learning

    Zhijian Liu;Haotian Tang;Yujun Lin;Song Han

  • Differentiable Augmentation for Data-Efficient GAN Training

    Shengyu Zhao;Zhijian Liu;Ji Lin;Jun-Yan Zhu

  • Fast inference of deep neural networks in FPGAs for particle physics

    Javier Duarte;Song Han;Philip Harris;Sergo Jindariani

  • MCUNet: Tiny Deep Learning on IoT Devices

    Ji Lin;Wei-Ming Chen;Yujun Lin;john cohn

Frequent Co-Authors

William J. Dally
William J. Dally Nvidia (United Kingdom)
Chuang Gan
Chuang Gan University of Massachusetts Amherst
Jun-Yan Zhu
Jun-Yan Zhu Carnegie Mellon University
Huazhong Yang
Huazhong Yang Tsinghua University
Bryan Catanzaro
Bryan Catanzaro Nvidia (United States)
Sertac Karaman
Sertac Karaman MIT
Mark Horowitz
Mark Horowitz Stanford University
Hae-Seung Lee
Hae-Seung Lee MIT
Daniela Rus
Daniela Rus MIT
Peter Bailis
Peter Bailis Stanford University

External Links

Personal website of Song Han Google Scholar page

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Related Online Degrees & Career Pathways

Exploring Computer Science in the USA often reveals a wide spectrum of related academic and career pathways. Many students opt for flexible learning through accredited online programs, allowing for better work-life balance and accessibility.

If advancing quickly into the workforce is your goal, pursuing one of the easy associate degrees can provide foundational skills and quicker job entry. For those aiming for advanced expertise, enrolling in online affordable master's programs in computer science or technology-related fields offers specialization without substantial financial burden.

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Best Scientists Citing Song Han

Yanzhi Wang

Yanzhi Wang

Northeastern University

Publications: 127

Luca Benini

Luca Benini

ETH Zurich

Publications: 65

Yuan Xie

Yuan Xie

Hong Kong University of Science and Technology

Publications: 61

Hai Li

Hai Li

Duke University

Publications: 57

Zhangyang Wang

Zhangyang Wang

The University of Texas at Austin

Publications: 56

Chang Xu

Chang Xu

University of Sydney

Publications: 54

Qi Tian

Qi Tian

Huawei Technologies (China)

Publications: 53

Yunhe Wang

Yunhe Wang

Huawei Technologies (China)

Publications: 52

Kaushik Roy

Kaushik Roy

Purdue University West Lafayette

Publications: 51

Kurt Keutzer

Kurt Keutzer

University of California, Berkeley

Publications: 48

Junjie Yan

Junjie Yan

SenseTime

Publications: 48

Rongrong Ji

Rongrong Ji

Xiamen University

Publications: 45

Yiyu Shi

Yiyu Shi

University of Notre Dame

Publications: 44

Nicholas D. Lane

Nicholas D. Lane

University of Cambridge

Publications: 43

Muhammad Shafique

Muhammad Shafique

New York University Abu Dhabi

Publications: 42

Baochang Zhang

Baochang Zhang

Beihang University

Publications: 41

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