World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
40
Citations
7934
World Ranking
9203
National Ranking
3917

Tolga Tasdizen 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 Tolga Tasdizen 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: 178 publications — 38th percentile

38% of scientists in this discipline score the same or lower.

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

Tolga Tasdizen 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 Tolga Tasdizen 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: 40 D-Index — 37th percentile

37% of scientists in this discipline score the same or lower.

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

Overview

Tolga Tasdizen is affiliated with the University of Utah in the United States. Their research spans several fields, primarily focusing on computer science and medicine. Within these domains, their notable subfields of study include artificial intelligence, computer vision and pattern recognition, radiology, nuclear medicine and imaging, transportation, and cardiology and cardiovascular medicine.

Their research topics cover a range of applications and interdisciplinary themes. These include:

  • AI in cancer detection
  • Urban transport and accessibility
  • Radiomics and machine learning in medical imaging
  • ECG monitoring and analysis
  • COVID-19 diagnosis using AI
  • Urban green space and health
  • Health disparities and outcomes

Tasdizen has collaborated frequently with several researchers, including:

  • Quynh C. Nguyen
  • Beatrice S. Knudsen
  • Luther W. McDonald
  • Ravi Ranjan
  • Ricardo Bigolin Lanfredi

Their publication record features papers in notable venues such as arXiv (Cornell University), Computing in Cardiology, International Journal of Environmental Research and Public Health, Microscopy and Microanalysis, and Medical Image Analysis.

Some recent publications include:

  • "Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases," 2020, International Journal of Environmental Research and Public Health
  • "Intelligent Damage Classification and Estimation in Power Distribution Poles Using Unmanned Aerial Vehicles and Convolutional Neural Networks," 2020, IEEE Transactions on Smart Grid
  • "Health and the built environment in United States cities: measuring associations using Google Street View-derived indicators of the built environment," 2020, BMC Public Health
  • "REFLACX, a dataset of reports and eye-tracking data for localization of abnormalities in chest x-rays," 2022, Scientific Data
  • "Unified generative adversarial networks for multimodal segmentation from unpaired 3D medical images," 2020, Medical Image Analysis

Best Publications

  • Regularization with stochastic transformations and perturbations for deep semi-supervised learning

    Mehdi Sajjadi;Mehran Javanmardi;Tolga Tasdizen

  • Curvature-based transfer functions for direct volume rendering: methods and applications

    G. Kindlmann;R. Whitaker;T. Tasdizen;T. Moller

  • Particle-Based Simulation of Fluids

    Simon Premoze;Tolga Tasdizen;James Bigler;Aaron E. Lefohn

  • Crowdsourcing the creation of image segmentation algorithms for connectomics

    Ignacio Arganda-Carreras;Srinivas C. Turaga;Daniel R. Berger;Dan Cireşan

  • Geometric surface smoothing via anisotropic diffusion of normals

    Tolga Tasdizen;Ross Whitaker;Paul Burchard;Stanley Osher

  • Principal Neighborhood Dictionaries for Nonlocal Means Image Denoising

    T. Tasdizen

  • Exploring the retinal connectome

    James R. Anderson;Bryan W. Jones;Carl B. Watt;Margaret V. Shaw

  • Geometric surface processing via normal maps

    Tolga Tasdizen;Ross Whitaker;Paul Burchard;Stanley Osher

  • A computational framework for ultrastructural mapping of neural circuitry.

    James R Anderson;Bryan W Jones;Jia-Hui Yang;Marguerite V Shaw

  • Manifold modeling for brain population analysis

    Samuel Gerber;Tolga Tasdizen;P. Thomas Fletcher;Sarang C. Joshi

  • Adaptive Markov modeling for mutual-information-based, unsupervised MRI brain-tissue classification.

    Suyash P. Awate;Tolga Tasdizen;Norman L. Foster;Ross T. Whitaker

  • Axon tracking in serial block-face scanning electron microscopy

    Elizabeth Jurrus;Melissa Hardy;Tolga Tasdizen;P. Thomas Fletcher

  • Temporally constrained reconstruction of dynamic cardiac perfusion MRI.

    Ganesh Adluru;Suyash P. Awate;Tolga Tasdizen;Ross T. Whitaker

  • Statistically quantitative volume visualization

    J.M. Kniss;R. Van Uitert;A. Stephens;G.-S. Li

  • Improving the stability of algebraic curves for applications

    T. Tasdizen;J.-P. Tarel;D.B. Cooper

  • Principal components for non-local means image denoising

    T. Tasdizen

  • Detection of neuron membranes in electron microscopy images using a serial neural network architecture.

    Elizabeth Jurrus;Elizabeth Jurrus;Antonio R.C. Paiva;Shigeki Watanabe;James R. Anderson

  • Decoding crystallography from high-resolution electron imaging and diffraction datasets with deep learning

    J. A. Aguiar;M. L. Gong;M. L. Gong;R. R. Unocic;T. Tasdizen

  • Domain adaptation for biomedical image segmentation using adversarial training

    Mehran Javanmardi;Tolga Tasdizen

  • Image Segmentation with Cascaded Hierarchical Models and Logistic Disjunctive Normal Networks

    Mojtaba Seyedhosseini;Mehdi Sajjadi;Tolga Tasdizen

Frequent Co-Authors

Ross T. Whitaker
Ross T. Whitaker University of Utah
Mujdat Cetin
Mujdat Cetin University of Rochester
Mark H. Ellisman
Mark H. Ellisman University of California, San Diego
Robert E. Marc
Robert E. Marc University of Utah
Norman L. Foster
Norman L. Foster University of Utah
Erik M. Jorgensen
Erik M. Jorgensen University of Utah
Julie R. Korenberg
Julie R. Korenberg University of Utah
Albert Cardona
Albert Cardona University of Cambridge
Andrew P. Lieberman
Andrew P. Lieberman University of Michigan–Ann Arbor
Raymond R. Unocic
Raymond R. Unocic Oak Ridge National Laboratory

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