World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
55
Citations
10855
World Ranking
4354
National Ranking
2036

Zhi-Pei Liang 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 Zhi-Pei Liang sits on this spectrum.

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 publications 991+

This scientist: 295 publications — 73rd percentile

73% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Zhi-Pei Liang 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 Zhi-Pei Liang sits on this spectrum.

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 D-Index 131+

This scientist: 55 D-Index — 71st percentile

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

The last bar groups every scientist with 131 D-Index or more.

Research.com Recognitions

  • 2006 - IEEE Fellow For contributions to biomedical applications of magnetic resonance imaging.

Overview

Zhi-Pei Liang is a researcher affiliated with the University of Illinois at Urbana-Champaign in the United States. Their work primarily focuses on the field of Medicine, with a significant number of publications related to Radiology, Nuclear Medicine and Imaging. Their research portfolio includes topics such as Advanced MRI Techniques and Applications, Medical Imaging Techniques and Applications, and Advanced Neuroimaging Techniques and Applications.

The scientist has contributed extensively to several specialized research areas including Advanced NMR Techniques and Applications, Atomic and Subatomic Physics Research, and NMR spectroscopy and applications. Their work also covers Medical Image Segmentation Techniques.

Frequent collaboration appears with several co-authors throughout their career. Notable collaborators include Yudu Li, Yibo Zhao, Rong Guo, Ziyu Meng, and Yao Li. These partnerships reflect recurring scholarly activity involving Magnetic Resonance in Medicine and related imaging techniques.

Many of Liang's publications have appeared across reputed venues, with multiple works published in:

  • Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
  • Magnetic Resonance in Medicine
  • IEEE Transactions on Biomedical Engineering
  • IEEE Transactions on Medical Imaging
  • Alzheimer's & Dementia

Some of their recent published papers include:

  • Fast high-resolution metabolic imaging of acute stroke with 3D magnetic resonance spectroscopy, 2020, Brain
  • Machine Learning-Enabled High-Resolution Dynamic Deuterium MR Spectroscopic Imaging, 2021, IEEE Transactions on Medical Imaging
  • Simultaneous QSM and metabolic imaging of the brain using SPICE: Further improvements in data acquisition and processing, 2020, Magnetic Resonance in Medicine
  • Accelerating T2mapping of the brain by integrating deep learning priors with low-rank and sparse modeling, 2020, Magnetic Resonance in Medicine
  • DeepSENSE: Learning coil sensitivity functions for SENSE reconstruction using deep learning, 2021, Magnetic Resonance in Medicine

Zhi-Pei Liang was awarded the IEEE Fellow honor in 2006 for contributions to biomedical applications of magnetic resonance imaging, highlighting their role in advancing this field.

Best Publications

  • SPATIOTEMPORAL IMAGINGWITH PARTIALLY SEPARABLE FUNCTIONS

    Zhi-Pei Liang

  • Accelerating advanced MRI reconstructions on GPUs

    S. S. Stone;J. P. Haldar;S. C. Tsao;W. m. W. Hwu

  • Robust water/fat separation in the presence of large field inhomogeneities using a graph cut algorithm.

    Diego Hernando;P. Kellman;J. P. Haldar;Z.-P. Liang

  • Image Reconstruction From Highly Undersampled $( {f k}, {t})$ -Space Data With Joint Partial Separability and Sparsity Constraints

    Bo Zhao;J. P. Haldar;A. G. Christodoulou;Zhi-Pei Liang

  • Compressed-Sensing MRI With Random Encoding

    J P Haldar;D Hernando;Zhi-Pei Liang

  • An efficient method for dynamic magnetic resonance imaging

    Zhi-Pei Liang;P.C. Lauterbur

  • Principles of magnetic resonance imaging

    Zhi-Pei Liang;Paul C. Lauterbur

  • Joint estimation of water/fat images and field inhomogeneity map

    Diego Hernando;J. P. Haldar;B. P. Sutton;Jingfei Ma

  • Spatiotemporal imaging with partially separable functions: A matrix recovery approach

    Justin P. Haldar;Zhi-Pei Liang

  • A generalized series approach to MR spectroscopic imaging

    Z.-P. Liang;P.C. Lauterbur

  • Accelerated MR parameter mapping with low-rank and sparsity constraints.

    Bo Zhao;Wenmiao Lu;T. Kevin Hitchens;Fan Lam

  • Parameter estimation of finite mixtures using the EM algorithm and information criteria with application to medical image processing

    Z. Liang;R.J. Jaszczak;R.E. Coleman

  • Denoising MR Spectroscopic Imaging Data With Low-Rank Approximations

    H. M. Nguyen;Xi Peng;M. N. Do;Zhi-Pei Liang

  • Chemical shift–based water/fat separation: A comparison of signal models

    Diego Hernando;Zhi Pei Liang;Peter Kellman

  • A subspace approach to high-resolution spectroscopic imaging

    Fan Lam;Zhi Pei Liang

  • Spatiotemporal Imaging with Partially Separable Functions

    Zhi-Pei Liang

  • Low rank matrix recovery for real-time cardiac MRI

    Bo Zhao;Justin P. Haldar;Cornelius Brinegar;Zhi-Pei Liang

  • Accelerating advanced mri reconstructions on gpus

    Samuel S. Stone;Justin P. Haldar;Stephanie C. Tsao;Wen-mei W. Hwu

  • Multiecho dixon fat and water separation method for detecting fibrofatty infiltration in the myocardium.

    Peter Kellman;Diego Hernando;Saurabh Shah;Sven Zuehlsdorff

  • A model-based method for phase unwrapping

    Zhi-Pei Liang

  • Accelerated High-Dimensional MR Imaging With Sparse Sampling Using Low-Rank Tensors

    Jingfei He;Qiegen Liu;Anthony G. Christodoulou;Chao Ma

  • Designing multichannel, multidimensional, arbitrary flip angle RF pulses using an optimal control approach.

    Dan Xu;Kevin F. King;Yudong Zhu;Graeme C. McKinnon

Frequent Co-Authors

Justin P. Haldar
Justin P. Haldar University of Southern California
Bradley P. Sutton
Bradley P. Sutton University of Illinois at Urbana-Champaign
Chien Ho
Chien Ho Carnegie Mellon University
Leslie Ying
Leslie Ying University at Buffalo, State University of New York
Wen-mei W. Hwu
Wen-mei W. Hwu University of Illinois at Urbana-Champaign
Thomas S. Huang
Thomas S. Huang University of Illinois at Urbana-Champaign
Norbert Schuff
Norbert Schuff University of California, San Francisco
E. Mark Haacke
E. Mark Haacke Wayne State University
Xi Peng
Xi Peng Sichuan University
Neal J. Cohen
Neal J. Cohen University of Illinois at Urbana-Champaign

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