World's Best Scientists 2026 revealed!
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Ming Yuan

D-Index & Metrics

Discipline name D-Index World Ranking Current World Ranking National Ranking Current National Ranking Publications Citations
Mathematics 50 1049 895 488 389 135 20259
Engineering and Technology 52 3529 3379 1035 947 129 20435

Ming Yuan publications per year

The chart shows the history of publications by Ming Yuan between 2002 and 2025, highlighting the no. of papers published in each year and offering an overview of the publication velocity of this scholar. Ming Yuan published across 24 years, from 2002 to 2025, averaging 9 papers a year. Output peaked at 35 publications in 2023. 28 of the 217 publications appeared in the last two years.

No. of publications
10 20 30
Bar chart. Horizontal axis: year, 2002 to 2025. Vertical axis: number of publications, 0 to 35. Peak 35 publications in 2023. 2002: 1 publication 2003: 2 publications 2004: 1 publication 2005: 3 publications 2006: 8 publications 2007: 11 publications 2008: 5 publications 2009: 5 publications 2010: 10 publications 2011: 11 publications 2012: 11 publications 2013: 2 publications 2014: 5 publications 2015: 8 publications 2016: 16 publications 2017: 7 publications 2018: 7 publications 2019: 11 publications 2020: 7 publications 2021: 13 publications 2022: 10 publications 2023: 35 publications 2024: 8 publications 2025: 20 publications
2002 2025

217 publications in total across all disciplines

View publications per year as a table
Ming Yuan: publications per year, 2002 to 2025
Year Publications
2002 1
2003 2
2004 1
2005 3
2006 8
2007 11
2008 5
2009 5
2010 10
2011 11
2012 11
2013 2
2014 5
2015 8
2016 16
2017 7
2018 7
2019 11
2020 7
2021 13
2022 10
2023 35
2024 8
2025 20
Total 217
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Ming Yuan publication distribution in Mathematics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mathematics in 2026. The highlighted bar marks where Ming Yuan sits on this spectrum.

No. of scientists
25 50 75 100
Bar chart with 100 bars. Horizontal axis: publications, 42–46 to 537+. Vertical axis: number of scientists, 0 to 112. Most scientists, 112, have 142–146 publications. The last bar groups every scientist with 537 publications or more. The highlighted bar, 132–136 publications, is where this scientist sits. 42–46 publications: 3 scientists 47–51 publications: 5 scientists 52–56 publications: 7 scientists 57–61 publications: 20 scientists 62–66 publications: 14 scientists 67–71 publications: 25 scientists 72–76 publications: 19 scientists 77–81 publications: 35 scientists 82–86 publications: 50 scientists 87–91 publications: 60 scientists 92–96 publications: 86 scientists 97–101 publications: 84 scientists 102–106 publications: 83 scientists 107–111 publications: 90 scientists 112–116 publications: 99 scientists 117–121 publications: 90 scientists 122–126 publications: 91 scientists 127–131 publications: 109 scientists 132–136 publications: 110 scientists 137–141 publications: 98 scientists 142–146 publications: 112 scientists 147–151 publications: 102 scientists 152–156 publications: 88 scientists 157–161 publications: 106 scientists 162–166 publications: 83 scientists 167–171 publications: 102 scientists 172–176 publications: 77 scientists 177–181 publications: 81 scientists 182–186 publications: 78 scientists 187–191 publications: 71 scientists 192–196 publications: 92 scientists 197–201 publications: 64 scientists 202–206 publications: 69 scientists 207–211 publications: 64 scientists 212–216 publications: 62 scientists 217–221 publications: 58 scientists 222–226 publications: 53 scientists 227–231 publications: 50 scientists 232–236 publications: 46 scientists 237–241 publications: 46 scientists 242–246 publications: 46 scientists 247–251 publications: 43 scientists 252–256 publications: 29 scientists 257–261 publications: 45 scientists 262–266 publications: 30 scientists 267–271 publications: 33 scientists 272–276 publications: 34 scientists 277–281 publications: 30 scientists 282–286 publications: 31 scientists 287–291 publications: 21 scientists 292–296 publications: 34 scientists 297–301 publications: 26 scientists 302–306 publications: 10 scientists 307–311 publications: 17 scientists 312–316 publications: 23 scientists 317–321 publications: 13 scientists 322–326 publications: 16 scientists 327–331 publications: 26 scientists 332–336 publications: 13 scientists 337–341 publications: 13 scientists 342–346 publications: 16 scientists 347–351 publications: 17 scientists 352–356 publications: 12 scientists 357–361 publications: 18 scientists 362–366 publications: 18 scientists 367–371 publications: 9 scientists 372–376 publications: 11 scientists 377–381 publications: 8 scientists 382–386 publications: 8 scientists 387–391 publications: 9 scientists 392–396 publications: 9 scientists 397–401 publications: 8 scientists 402–406 publications: 11 scientists 407–411 publications: 6 scientists 412–416 publications: 6 scientists 417–421 publications: 9 scientists 422–426 publications: 8 scientists 427–431 publications: 5 scientists 432–436 publications: 8 scientists 437–441 publications: 8 scientists 442–446 publications: 4 scientists 447–451 publications: 4 scientists 452–456 publications: 4 scientists 457–461 publications: 2 scientists 462–466 publications: 2 scientists 467–471 publications: 4 scientists 472–476 publications: 3 scientists 477–481 publications: 3 scientists 482–486 publications: 6 scientists 487–491 publications: 3 scientists 492–496 publications: 5 scientists 497–501 publications: 5 scientists 502–506 publications: 1 scientist 507–511 publications: 6 scientists 512–516 publications: 4 scientists 517–521 publications: 1 scientist 522–526 publications: 3 scientists 527–531 publications: 1 scientist 532–536 publications: 4 scientists 537+ publications: 100 scientists
42–46 publications 537+

This scientist: 135 publications — 29th percentile

29% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 537 publications or more.

View publications distribution as a table
Number of Mathematics scientists by publication count, Research.com 2026 ranking edition. Based on 3,584 ranked scientists.
Publications Scientists This scientist
42–46 3
47–51 5
52–56 7
57–61 20
62–66 14
67–71 25
72–76 19
77–81 35
82–86 50
87–91 60
92–96 86
97–101 84
102–106 83
107–111 90
112–116 99
117–121 90
122–126 91
127–131 109
132–136 110 135
137–141 98
142–146 112
147–151 102
152–156 88
157–161 106
162–166 83
167–171 102
172–176 77
177–181 81
182–186 78
187–191 71
192–196 92
197–201 64
202–206 69
207–211 64
212–216 62
217–221 58
222–226 53
227–231 50
232–236 46
237–241 46
242–246 46
247–251 43
252–256 29
257–261 45
262–266 30
267–271 33
272–276 34
277–281 30
282–286 31
287–291 21
292–296 34
297–301 26
302–306 10
307–311 17
312–316 23
317–321 13
322–326 16
327–331 26
332–336 13
337–341 13
342–346 16
347–351 17
352–356 12
357–361 18
362–366 18
367–371 9
372–376 11
377–381 8
382–386 8
387–391 9
392–396 9
397–401 8
402–406 11
407–411 6
412–416 6
417–421 9
422–426 8
427–431 5
432–436 8
437–441 8
442–446 4
447–451 4
452–456 4
457–461 2
462–466 2
467–471 4
472–476 3
477–481 3
482–486 6
487–491 3
492–496 5
497–501 5
502–506 1
507–511 6
512–516 4
517–521 1
522–526 3
527–531 1
532–536 4
537+ 100
Download as CSV

Ming Yuan D-index placement in Mathematics in 2026

The chart shows the D-index (discipline H-index) distribution of Mathematics scientists ranked by Research.com in 2026. The highlighted bar marks where Ming Yuan sits on this spectrum.

No. of scientists
50 100 150
Bar chart with 57 bars. Horizontal axis: D-Index, 30 to 86+. Vertical axis: number of scientists, 0 to 174. Most scientists, 174, have 30 D-Index. The last bar groups every scientist with 86 D-Index or more. The highlighted bar, 50 D-Index, is where this scientist sits. 30 D-Index: 174 scientists 31 D-Index: 151 scientists 32 D-Index: 174 scientists 33 D-Index: 117 scientists 34 D-Index: 136 scientists 35 D-Index: 127 scientists 36 D-Index: 145 scientists 37 D-Index: 153 scientists 38 D-Index: 150 scientists 39 D-Index: 150 scientists 40 D-Index: 138 scientists 41 D-Index: 136 scientists 42 D-Index: 93 scientists 43 D-Index: 108 scientists 44 D-Index: 115 scientists 45 D-Index: 112 scientists 46 D-Index: 103 scientists 47 D-Index: 75 scientists 48 D-Index: 59 scientists 49 D-Index: 67 scientists 50 D-Index: 60 scientists 51 D-Index: 57 scientists 52 D-Index: 59 scientists 53 D-Index: 62 scientists 54 D-Index: 60 scientists 55 D-Index: 50 scientists 56 D-Index: 42 scientists 57 D-Index: 54 scientists 58 D-Index: 50 scientists 59 D-Index: 42 scientists 60 D-Index: 41 scientists 61 D-Index: 35 scientists 62 D-Index: 40 scientists 63 D-Index: 21 scientists 64 D-Index: 31 scientists 65 D-Index: 27 scientists 66 D-Index: 29 scientists 67 D-Index: 19 scientists 68 D-Index: 25 scientists 69 D-Index: 17 scientists 70 D-Index: 18 scientists 71 D-Index: 12 scientists 72 D-Index: 14 scientists 73 D-Index: 13 scientists 74 D-Index: 18 scientists 75 D-Index: 9 scientists 76 D-Index: 11 scientists 77 D-Index: 10 scientists 78 D-Index: 9 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 10 scientists 82 D-Index: 5 scientists 83 D-Index: 5 scientists 84 D-Index: 13 scientists 85 D-Index: 6 scientists 86+ D-Index: 99 scientists
30 D-Index 86+

This scientist: 50 D-Index — 71st percentile

71% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 86 D-Index or more.

View D-Index distribution as a table
Number of Mathematics scientists by D-index, Research.com 2026 ranking edition. Based on 3,584 ranked scientists.
D-Index Scientists This scientist
30 174
31 151
32 174
33 117
34 136
35 127
36 145
37 153
38 150
39 150
40 138
41 136
42 93
43 108
44 115
45 112
46 103
47 75
48 59
49 67
50 60 50
51 57
52 59
53 62
54 60
55 50
56 42
57 54
58 50
59 42
60 41
61 35
62 40
63 21
64 31
65 27
66 29
67 19
68 25
69 17
70 18
71 12
72 14
73 13
74 18
75 9
76 11
77 10
78 9
79 16
80 12
81 10
82 5
83 5
84 13
85 6
86+ 99
Download as CSV

Overview

Ming Yuan is affiliated with Columbia University in the United States and has a research profile spanning various fields within engineering and computer science. Their work encompasses significant contributions across multiple disciplines, including artificial intelligence, computational mathematics, computational mechanics, molecular biology, and statistics and probability.

The scientist's research topics cover a diverse range of advanced and technical areas. These include:

  • Tensor decomposition and applications
  • Sparse and compressive sensing techniques
  • Blind source separation techniques
  • Statistical methods and inference
  • Financial markets and investment strategies
  • Advanced biosensing and bioanalysis techniques
  • Biosensors and analytical detection

Ming Yuan has published research extensively in venues such as arXiv (Cornell University), SSRN Electronic Journal, The Annals of Statistics, Journal of the American Statistical Association, and Journal of the Royal Statistical Society Series B (Statistical Methodology). These publications reflect a consistent output in both theoretical and applied statistical research alongside interdisciplinary engineering domains.

Notable recent papers include:

  • A label-free aptasensor for turn-on fluorescent detection of ochratoxin a based on SYBR gold and single walled carbon nanohorns, 2020, Food Control
  • Statistically optimal and computationally efficient low rank tensor completion from noisy entries, 2021, The Annals of Statistics
  • ISLET: Fast and Optimal Low-Rank Tensor Regression via Importance Sketching, 2020, SIAM Journal on Mathematics of Data Science
  • Controlling the minimal feature sizes in adjoint optimization of nanophotonic devices using b-spline surfaces, 2020, Optics Express
  • Statistical Inferences of Linear Forms for Noisy Matrix Completion, 2020, Journal of the Royal Statistical Society Series B (Statistical Methodology)

Ming Yuan has collaborated frequently with a core group of coauthors. The most frequent collaborators include Arnab Auddy, Dong Xia, Anru R. Zhang, Guofu Zhou, and Jungjun Choi. These repeated collaborations suggest a focused research network within the domains of statistics, tensor methods, and applied mathematics.

Best Publications

  • Model selection and estimation in regression with grouped variables

    Ming Yuan;Yi Lin

  • Model selection and estimation in the Gaussian graphical model

    Ming Yuan;Yi Lin

  • Composite quantile regression and the oracle model selection theory

    Y. Hui Zou;Ming Yuan

  • High Dimensional Semiparametric Gaussian Copula Graphical Models.

    Han Liu;Fang Han;Ming Yuan;John D. Lafferty

  • High Dimensional Inverse Covariance Matrix Estimation via Linear Programming

    Ming Yuan

  • On the non-negative garrotte estimator

    Ming Yuan;Yi Lin

  • Dimension reduction and coefficient estimation in multivariate linear regression

    Ming Yuan;Ali Ekici;Zhaosong Lu;Renato Monteiro

  • CARM1 Methylates Chromatin Remodeling Factor BAF155 to Enhance Tumor Progression and Metastasis

    Lu Wang;Zibo Zhao;Mark B. Meyer;Sandeep Saha

  • A Reproducing Kernel Hilbert Space Approach to Functional Linear Regression

    Ming Yuan;T. Tony Cai

  • On Tensor Completion via Nuclear Norm Minimization

    Ming Yuan;Cun-Hui Zhang

  • Efficient Empirical Bayes Variable Selection and Estimation in Linear Models

    Ming Yuan;Yi Lin

  • A direct approach to sparse discriminant analysis in ultra-high dimensions

    Qing Mai;Hui Zou;Ming Yuan

  • Minimax and Adaptive Prediction for Functional Linear Regression

    T. Tony Cai;Ming Yuan

  • Statistical methods for expression quantitative trait loci (eQTL) mapping.

    C. M. Kendziorski;M. Chen;M. Yuan;H. Lan

  • SPARSITY IN MULTIPLE KERNEL LEARNING

    Vladimir Koltchinskii;Ming Yuan

  • GACV for quantile smoothing splines

    Ming Yuan

  • Learning Networks of Heterogeneous Influence

    Nan Du;Le Song;Ming Yuan;Alex J. Smola

  • Doubly Robust Learning for Estimating Individualized Treatment with Censored Data

    Ying-Qi Zhao;Donglin Zeng;Eric B Laber;Rui Song

  • Nanophotonic media for artificial neural inference

    Erfan Khoram;Ang Chen;Dianjing Liu;Lei Ying

  • Quantitating the cell: turning images into numbers with ImageJ.

    Ellen T Arena;Ellen T Arena;Curtis T Rueden;Mark C Hiner;Shulei Wang

  • Classification Methods with Reject Option Based on Convex Risk Minimization

    Ming Yuan;Marten Wegkamp

  • The Nonparanormal SKEPTIC

    Han Liu;Fang Han;Ming Yuan;Larry Wasserman

Frequent Co-Authors

Tony Cai
Tony Cai University of Pennsylvania
Zongfu Yu
Zongfu Yu University of Wisconsin–Madison
Hui Zou
Hui Zou University of Minnesota
Han Liu
Han Liu Northwestern University
Larry Wasserman
Larry Wasserman Carnegie Mellon University
Cun-Hui Zhang
Cun-Hui Zhang Rutgers, The State University of New Jersey
Paul Ahlquist
Paul Ahlquist University of Wisconsin–Madison
Christina Kendziorski
Christina Kendziorski University of Wisconsin–Madison
Renato D. C. Monteiro
Renato D. C. Monteiro Georgia Institute of Technology
David L. Page
David L. Page MIT

External Links

Personal website of Ming Yuan Google Scholar page

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By leveraging these related fields and program types, Mathematics students can tailor their education to suit career goals, gaining valuable interdisciplinary skills and expanding future opportunities.

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Publications: 51

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Francis Bach

École Normale Supérieure

Publications: 51

Eric P. Xing

Eric P. Xing

Mohamed bin Zayed University of Artificial Intelligence

Publications: 50

Jianqing Fan

Jianqing Fan

Princeton University

Publications: 49

Jieping Ye

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Alibaba Group (China)

Publications: 43

Martin J. Wainwright

Martin J. Wainwright

MIT

Publications: 42

Moeness G. Amin

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Publications: 32

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Publications: 32

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Publications: 30

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Publications: 29

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Publications: 29

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Publications: 29

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