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Guang Lin

D-Index & Metrics

Discipline name D-Index World Ranking Current World Ranking National Ranking Current National Ranking Publications Citations
Computer Science 38 10373 10082 4338 4160 188 5005

Guang Lin publications per year

The chart shows the history of publications by Guang Lin between 1988 and 2025, highlighting the no. of papers published in each year and offering an overview of the publication velocity of this scholar. Guang Lin published across 38 years, from 1988 to 2025, averaging 9.8 papers a year. Output peaked at 53 publications in 2025. 88 of the 374 publications appeared in the last two years.

No. of publications
10 20 30 40 50
Bar chart. Horizontal axis: year, 1988 to 2025. Vertical axis: number of publications, 0 to 53. Peak 53 publications in 2025. 1988: 1 publication 1989: 0 publications 1990: 0 publications 1991: 0 publications 1992: 0 publications 1993: 0 publications 1994: 0 publications 1995: 0 publications 1996: 0 publications 1997: 0 publications 1998: 0 publications 1999: 0 publications 2000: 0 publications 2001: 1 publication 2002: 1 publication 2003: 0 publications 2004: 1 publication 2005: 2 publications 2006: 3 publications 2007: 4 publications 2008: 2 publications 2009: 3 publications 2010: 2 publications 2011: 5 publications 2012: 13 publications 2013: 16 publications 2014: 22 publications 2015: 15 publications 2016: 15 publications 2017: 23 publications 2018: 20 publications 2019: 19 publications 2020: 27 publications 2021: 36 publications 2022: 20 publications 2023: 35 publications 2024: 35 publications 2025: 53 publications
1988 2025

374 publications in total across all disciplines

View publications per year as a table
Guang Lin: publications per year, 1988 to 2025
Year Publications
1988 1
1989 0
1990 0
1991 0
1992 0
1993 0
1994 0
1995 0
1996 0
1997 0
1998 0
1999 0
2000 0
2001 1
2002 1
2003 0
2004 1
2005 2
2006 3
2007 4
2008 2
2009 3
2010 2
2011 5
2012 13
2013 16
2014 22
2015 15
2016 15
2017 23
2018 20
2019 19
2020 27
2021 36
2022 20
2023 35
2024 35
2025 53
Total 374
Download as CSV

Guang Lin 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 Guang Lin 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, 182–191 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: 188 publications — 42nd percentile

42% 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
182–191 583 188
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

Guang Lin 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 Guang Lin 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

Overview

Guang Lin is affiliated with Purdue University West Lafayette in the United States. Their research spans multiple domains primarily concentrated in Engineering and Computer Science, with a significant focus on Artificial Intelligence and related subfields.

Their work covers several specialized areas, including:

  • Model Reduction and Neural Networks
  • Probabilistic and Robust Engineering Design
  • Markov Chains and Monte Carlo Methods
  • Gaussian Processes and Bayesian Inference
  • Machine Learning in Materials Science
  • Advanced Mathematical Modeling in Engineering
  • Fluid Dynamics and Turbulent Flows

Guang Lin has published extensively in prominent venues such as:

  • arXiv (Cornell University)
  • Journal of Computational Physics
  • SSRN Electronic Journal
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Journal of Computational and Applied Mathematics

Their research outputs include numerous papers co-authored with collaborators like Christian Moya, Zecheng Zhang, Ziyang Huang, Haoyang Zheng, and Yixuan Sun.

Some of the recent papers include:

  • EEG-based emotion recognition using 4D convolutional recurrent neural network (2020), published in Cognitive Neurodynamics
  • A transfer Learning-Based LSTM strategy for imputing Large-Scale consecutive missing data and its application in a water quality prediction system (2021), published in Journal of Hydrology
  • Comparison of physical-based, data-driven and hybrid modeling approaches for evapotranspiration estimation (2021), published in Journal of Hydrology
  • Identifiability and predictability of integer- and fractional-order epidemiological models using physics-informed neural networks (2021), published in Nature Computational Science
  • Improving Simulation Efficiency of MCMC for Inverse Modeling of Hydrologic Systems With a Kalman-Inspired Proposal Distribution (2020), published in Water Resources Research

Guang Lin's contributions integrate computational techniques and advanced mathematical modeling approaches across various scientific disciplines, reflecting a multidisciplinary engagement between engineering and computer science. Their collaboration network and publication record emphasize consistent active participation in fields where Artificial Intelligence and scientific computing intersect.

Best Publications

  • Multi-output separable Gaussian process: Towards an efficient, fully Bayesian paradigm for uncertainty quantification

    Ilias Bilionis;Nicholas Zabaras;Bledar A. Konomi;Guang Lin

  • Compressive Sensing Based Machine Learning Strategy For Characterizing The Flow Around A Cylinder With Limited Pressure Measurements

    Ido Bright;Guang Lin;J. Nathan Kutz

  • Robust data-driven discovery of governing physical laws with error bars.

    Sheng Zhang;Guang Lin

  • Adaptive ANOVA decomposition of stochastic incompressible and compressible flows

    Xiu Yang;Minseok Choi;Guang Lin;George Em Karniadakis

  • Sensitivity of surface flux simulations to hydrologic parameters based on an uncertainty quantification framework applied to the Community Land Model

    Zhangshuan Hou;Maoyi Huang;L. Ruby Leung;Guang Lin

  • An efficient, high-order probabilistic collocation method on sparse grids for three-dimensional flow and solute transport in randomly heterogeneous porous media

    Guang Lin;Alexandre M. Tartakovsky

  • Comparison of physical-based, data-driven and hybrid modeling approaches for evapotranspiration estimation

    Xiaolong Hu;Liangsheng Shi;Guang Lin;Lin Lin

  • Infrared Thermal Imaging-Based Crack Detection Using Deep Learning

    Jun Yang;Wei Wang;Guang Lin;Qing Li

  • Multi-Resolution Climate Ensemble Parameter Analysis with Nested Parallel Coordinates Plots

    Junpeng Wang;Xiaotong Liu;Han-Wei Shen;Guang Lin

  • An Iterative Local Updating Ensemble Smoother for Estimation and Uncertainty Assessment of Hydrologic Model Parameters With Multimodal Distributions

    Jiangjiang Zhang;Guang Lin;Weixuan Li;Laosheng Wu

  • Identifiability and predictability of integer- and fractional-order epidemiological models using physics-informed neural networks

    Ehsan Kharazmi;Min Cai;Min Cai;Xiaoning Zheng;Xiaoning Zheng;Zhen Zhang

  • ConvPDE-UQ: Convolutional neural networks with quantified uncertainty for heterogeneous elliptic partial differential equations on varied domains

    Nick Winovich;Karthik Ramani;Guang Lin

  • A sensitivity analysis of cloud properties to CLUBB parameters in the single-column Community Atmosphere Model (SCAM5)

    Zhun Guo;Zhun Guo;Minghuai Wang;Yun Qian;Vincent E. Larson

  • Improving Simulation Efficiency of MCMC for Inverse Modeling of Hydrologic Systems With a Kalman-Inspired Proposal Distribution

    Jiangjiang Zhang;Jasper A. Vrugt;Jasper A. Vrugt;Xiaoqing Shi;Guang Lin

  • Generating Random Earthquake Events for Probabilistic Tsunami Hazard Assessment

    Randall J. LeVeque;Knut Waagan;Frank I. González;Donsub Rim

  • Predicting shock dynamics in the presence of uncertainties

    G. Lin;C.-H. Su;G. E. Karniadakis

  • Weak Galerkin finite element methods for Darcy flow: Anisotropy and heterogeneity

    Guang Lin;Guang Lin;Jiangguo Liu;Lin Mu;Xiu Ye

  • A Sensitivity Study of Radiative Fluxes at the Top of Atmosphere to Cloud-Microphysics and Aerosol Parameters in the Community Atmosphere Model CAM5

    Chun Zhao;Xiaohong Liu;Xiaohong Liu;Yun Qian;Jin-Ho Yoon

  • Dynamic-Feature Extraction, Attribution, and Reconstruction (DEAR) Method for Power System Model Reduction

    Shaobu Wang;Shuai Lu;Ning Zhou;Guang Lin

  • Uncertainty quantification via random domain decomposition and probabilistic collocation on sparse grids

    G. Lin;A. M. Tartakovsky;D. M. Tartakovsky

  • DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving

    Wei Deng;Junwei Pan;Tian Zhou;Deguang Kong

Frequent Co-Authors

George Em Karniadakis
George Em Karniadakis Brown University
Laosheng Wu
Laosheng Wu University of California, Riverside
Faming Liang
Faming Liang Purdue University West Lafayette
Yun Qian
Yun Qian Pacific Northwest National Laboratory
Jasper A. Vrugt
Jasper A. Vrugt University of California, Irvine
Jie Bao
Jie Bao University of New South Wales
Xiaohong Liu
Xiaohong Liu Texas A&M University
Alexandre M. Tartakovsky
Alexandre M. Tartakovsky University of Illinois at Urbana-Champaign
Chun Zhao
Chun Zhao University of Science and Technology of China
Karthik Ramani
Karthik Ramani Purdue University West Lafayette

External Links

Personal website of Guang Lin

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Related Online Degrees & Career Pathways

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Best Scientists Citing Guang Lin

J. Nathan Kutz

J. Nathan Kutz

University of Washington

Publications: 37

Yun Qian

Yun Qian

Pacific Northwest National Laboratory

Publications: 31

George Em Karniadakis

George Em Karniadakis

Brown University

Publications: 28

Nicholas Zabaras

Nicholas Zabaras

University of Notre Dame

Publications: 28

Maoyi Huang

Maoyi Huang

Pacific Northwest National Laboratory

Publications: 24

Dongxiao Zhang

Dongxiao Zhang

Southern University of Science and Technology

Publications: 23

Alexandre M. Tartakovsky

Alexandre M. Tartakovsky

University of Illinois at Urbana-Champaign

Publications: 18

Qingyun Duan

Qingyun Duan

Hohai University

Publications: 15

Laosheng Wu

Laosheng Wu

University of California, Riverside

Publications: 14

Lai-yung Ruby Leung

Lai-yung Ruby Leung

Pacific Northwest National Laboratory

Publications: 14

Minghuai Wang

Minghuai Wang

Nanjing University

Publications: 14

Vincent E. Larson

Vincent E. Larson

University of Wisconsin–Milwaukee

Publications: 12

Alberto Guadagnini

Alberto Guadagnini

Polytechnic University of Milan

Publications: 11

Philip J. Rasch

Philip J. Rasch

Pacific Northwest National Laboratory

Publications: 11

Nathan A. Baker

Nathan A. Baker

Pacific Northwest National Laboratory

Publications: 10

Jichun Wu

Jichun Wu

Nanjing University

Publications: 10

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