World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
32
Citations
5488
World Ranking
3151
National Ranking
1263

Computer Science

D-Index
33
Citations
5850
World Ranking
12512
National Ranking
5079

Faming Liang 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 Faming Liang sits on this spectrum.

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 scientists 507–511 publications: 6 scientists 512–516 publications: 4 scientists 517–521 publications: 1 scientists 522–526 publications: 3 scientists 527–531 publications: 1 scientists 532–536 publications: 4 scientists 537+ publications: 100 scientists
42 publications 537+

This scientist: 151 publications — 39th percentile

39% of scientists in this discipline score the same or lower.

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

Faming Liang 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 Faming Liang sits on this spectrum.

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: 32 D-Index — 14th percentile

14% of scientists in this discipline score the same or lower.

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

Overview

Faming Liang is affiliated with Purdue University West Lafayette in the United States. Their research spans the fields of Computer Science and Mathematics, with a significant focus on specialized subfields including Artificial Intelligence, Statistics and Probability, Computer Vision and Pattern Recognition, Molecular Biology, and Statistical and Nonlinear Physics.

The main topics covered in their work include Markov Chains and Monte Carlo Methods, Gaussian Processes and Bayesian Inference, Statistical Methods and Inference, Stochastic Gradient Optimization Techniques, Model Reduction and Neural Networks, Generative Adversarial Networks and Image Synthesis, and Gene Expression and Cancer Classification.

Faming Liang has contributed to various publication venues, with numerous articles appearing on arXiv (Cornell University). Other frequent venues include Statistica Sinica, Journal of Computational and Graphical Statistics, Journal of the American Statistical Association, and Journal of Statistical Computation and Simulation.

Notable recent papers authored by or coauthored with Faming Liang include:

  • "Nearly optimal Bayesian shrinkage for high-dimensional regression," 2022, published in Science China Mathematics
  • "Nonlinear Variable Selection via Deep Neural Networks," 2020, Journal of Computational and Graphical Statistics
  • "Consistent Sparse Deep Learning: Theory and Computation," 2021, Journal of the American Statistical Association
  • "Extended stochastic gradient Markov chain Monte Carlo for large-scale Bayesian variable selection," 2020, Biometrika
  • "Non-convex Learning via Replica Exchange Stochastic Gradient MCMC," 2020, PubMed

Faming Liang frequently collaborates with several coauthors, including Yan Sun, Qifan Song, Sehwan Kim, Wei Deng, and Guang Lin. These collaborations have resulted in numerous joint publications contributing to the overview of their research topics and methodologies.

Best Publications

  • The Multiple-Try Method and Local Optimization in Metropolis Sampling

    Jun S. Liu;Faming Liang;Wing Hung Wong

  • Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples

    Faming Liang;Chuanhai Liu;Raymond J. Carroll

  • Stochastic Approximation in Monte Carlo Computation

    Faming Liang;Chuanhai Liu;Raymond J Carroll

  • Real-Parameter Evolutionary Monte Carlo With Applications to Bayesian Mixture Models

    Faming Liang;Wing Hung Wong

  • Evolutionary Monte Carlo for protein folding simulations

    Faming Liang;Wing Hung Wong

  • Crash Injury Severity Analysis Using Bayesian Ordered Probit Models

    Yuanchang Xie;Yuanchang Xie;Yunlong Zhang;Yunlong Zhang;Faming Liang;Faming Liang

  • Comprehensive Computational Pathological Image Analysis Predicts Lung Cancer Prognosis

    Xin Luo;Xiao Zang;Lin Yang;Lin Yang;Junzhou Huang

  • A double Metropolis–Hastings sampler for spatial models with intractable normalizing constants

    Faming Liang

  • Statistical and Computational Inverse Problems

    Faming Liang;Jianhua Huang

  • Enhanced construction of gene regulatory networks using hub gene information.

    Donghyeon Yu;Johan Lim;Xinlei Wang;Faming Liang

  • Bayesian neural networks for nonlinear time series forecasting

    Faming Liang

  • Dynamic weighting in Monte Carlo and optimization

    Wing Hung Wong;Faming Liang

  • Estimating uncertainty of streamflow simulation using Bayesian neural networks

    Xuesong Zhang;Faming Liang;Raghavan Srinivasan;Michael Van Liew

  • Bayesian Neural Networks for Selection of Drug Sensitive Genes

    Faming Liang;Qizhai Li;Lei Zhou

  • Bayesian Subset Modeling for High-Dimensional Generalized Linear Models

    Faming Liang;Qifan Song;Kai Yu

  • A Resampling-Based Stochastic Approximation Method for Analysis of Large Geostatistical Data

    Faming Liang;Yichen Cheng;Qifan Song;Jincheol Park

  • A split‐and‐merge Bayesian variable selection approach for ultrahigh dimensional regression

    Qifan Song;Faming Liang

  • Markov Chain Monte Carlo: Innovations and Applications

    W S Kendall;F Liang;J-S Wang

  • A Generalized Wang–Landau Algorithm for Monte Carlo Computation

    Faming Liang

  • Dynamically Weighted Importance Sampling in Monte Carlo Computation

    Faming Liang

  • Crash Injury Severity Analysis Using a Bayesian Ordered Probit Model

    Yunlong Zhang;Faming Liang;Yuanchang Xie

Frequent Co-Authors

Wing Hung Wong
Wing Hung Wong Stanford University
Raymond J. Carroll
Raymond J. Carroll Texas A&M University
Guang Lin
Guang Lin Purdue University West Lafayette
Jun S. Liu
Jun S. Liu Harvard University
Pieter A. Doevendans
Pieter A. Doevendans Utrecht University
Kai Yu
Kai Yu Horizon Robotics Inc.
Jason Cong
Jason Cong University of California, Los Angeles
Yolanda van der Graaf
Yolanda van der Graaf Utrecht University
Yunlong Zhang
Yunlong Zhang Texas A&M University
Diederick E. Grobbee
Diederick E. Grobbee Utrecht University

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