World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
40
Citations
7428
World Ranking
2033
National Ranking
860

Hua 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 Hua 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: 188 publications — 57th percentile

57% of scientists in this discipline score the same or lower.

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

Hua 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 Hua 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: 40 D-Index — 45th percentile

45% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2013 - Fellow of the American Statistical Association (ASA)

Overview

Hua Liang is affiliated with George Washington University in the United States, contributing extensively to the field of Mathematics, with a focus on subfields such as Statistics and Probability, Artificial Intelligence, Management Science and Operations Research, and Genetics.

Their research encompasses several main topics including:

  • Statistical Methods and Inference
  • Statistical Methods and Bayesian Inference
  • Advanced Statistical Methods and Models
  • Statistical Methods in Clinical Trials
  • Bayesian Methods and Mixture Models
  • Probabilistic and Robust Engineering Design
  • Control Systems and Identification

Hua Liang has authored numerous papers, with notable recent publications including:

  • "SARS-CoV-2-specific T cells are rapidly expanded for therapeutic use and target conserved regions of the membrane protein," 2020, Blood
  • "Outcome of donor-derived TAA-T cell therapy in patients with high-risk or relapsed acute leukemia post allogeneic BMT," 2022, Blood Advances
  • "Simultaneous confidence intervals for ratios of means of zero-inflated log-normal populations," 2021, Journal of Statistical Computation and Simulation
  • "The impact of pre-existing HLA and red blood cell antibodies on transfusion support and engraftment in sickle cell disease after nonmyeloablative hematopoietic stem cell transplantation from HLA-matched sibling donors: A prospective, single-center, observational study," 2020, EClinicalMedicine
  • "Estimation and inference in partially functional linear regression with multiple functional covariates," 2020, Journal of Statistical Planning and Inference

Their work has appeared frequently in several publication venues, including:

  • Statistica Sinica
  • arXiv (Cornell University)
  • Blood
  • Statistics and Computing
  • SSRN Electronic Journal

Frequent collaborators in Hua Liang's research include:

  • Xinmin Li
  • Xinyu Zhang
  • Feifei Chen
  • Dalei Yu
  • Shunyao Wu

In recognition of their work in the field of statistics, Hua Liang was named a Fellow of the American Statistical Association in 2013.

Best Publications

  • Partially Linear Models

    Wolfgang Hardle;Hua LIang;Jiti Gao

  • Estimation in a semiparametric partially linear errors-in-variables model

    Hua Liang;Wolfgang Härdle;Raymond J. Carroll

  • Variable Selection in Semiparametric Regression Modeling.

    Runze Li;Hua Liang

  • Parameter Estimation for Differential Equation Models Using a Framework of Measurement Error in Regression Models

    Hua Liang;Hulin Wu

  • ESTIMATION AND TESTING FOR PARTIALLY LINEAR SINGLE-INDEX MODELS.

    Hua Liang;Xiang Liu;Runze Li;Chih Ling Tsai

  • Variable Selection for Partially Linear Models with Measurement Errors.

    Hua Liang;Runze Li

  • Optimal Weight Choice for Frequentist Model Average Estimators

    Hua Liang;Guohua Zou;Alan T. K. Wan;Xinyu Zhang

  • Safety and immunogenicity of a baculovirus-expressed hemagglutinin influenza vaccine: a randomized controlled trial.

    John J. Treanor;Gilbert M. Schiff;Frederick G. Hayden;Rebecca C. Brady

  • Focused information criterion and model averaging for generalized additive partial linear models

    Xinyu Zhang;Hua Liang

  • Optimal Model Averaging Estimation for Generalized Linear Models and Generalized Linear Mixed-Effects Models

    Xinyu Zhang;Dalei Yu;Guohua Zou;Hua Liang

  • Partially linear models with missing response variables and error-prone covariates

    Hua Liang;Suojin Wang;Raymond J. Carroll

  • A note on conditional AIC for linear mixed-effects models

    Hua Liang;Hulin Wu;Guohua Zou

  • The relationship between virologic and immunologic responses in AIDS clinical research using mixed-effects varying-coefficient models with measurement error.

    Hua Liang;Hulin Wu;Raymond J. Carroll

  • Estimation in Partially Linear Models With Missing Covariates

    Hua Liang;Suojin Wang;James M Robins;Raymond J Carroll

  • Statistical inference for semiparametric varying-coefficient partially linear models with error-prone linear covariates

    Yong Zhou;Hua Liang

  • Quantile Regression Estimates for a Class of Linear and Partially Linear Errors-in-Variables Models

    Xuming He;Hua Liang

  • Estimation and Variable Selection for Semiparametric Additive Partial Linear Models (SS-09-140)

    Xiang Liu;Li Wang;Hua Liang

  • ESTIMATION AND VARIABLE SELECTION FOR GENERALIZED ADDITIVE PARTIAL LINEAR MODELS

    Li Wang;Xiang Liu;Hua Liang;Raymond J. Carroll

  • Additive Partial Linear Models with Measurement Errors

    Hua Liang;Sally W. Thurston;David Ruppert;Tatiyana Apanasovich

  • Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling

    Hulin Wu;Tao Lu;Hongqi Xue;Hua Liang

  • Estimation and variable selection for generalized additive partial linear models

    Li Wang;Xiang Liu;Hua Liang;Raymond J. Carroll

Frequent Co-Authors

Hulin Wu
Hulin Wu The University of Texas Health Science Center at Houston
Wolfgang Karl Härdle
Wolfgang Karl Härdle Humboldt-Universität zu Berlin
Raymond J. Carroll
Raymond J. Carroll Texas A&M University
David Ruppert
David Ruppert Cornell University
Jiti Gao
Jiti Gao Monash University
Lixing Zhu
Lixing Zhu Beijing Normal University
Robert J. Wilkinson
Robert J. Wilkinson The Francis Crick Institute
Runze Li
Runze Li Pennsylvania State University
John J. Treanor
John J. Treanor University of Rochester Medical Center
Mary Carrington
Mary Carrington Ragon Institute of MGH, MIT and Harvard

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