World's Best Scientists 2026 revealed!
Ulisses M. Braga-Neto

Ulisses M. Braga-Neto

D-Index & Metrics

Computer Science

D-Index
36
Citations
4888
World Ranking
11339
National Ranking
4674

Ulisses M. Braga-Neto 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 Ulisses M. Braga-Neto 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: 176 publications — 37th percentile

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

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

Ulisses M. Braga-Neto 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 Ulisses M. Braga-Neto 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: 36 D-Index — 23rd percentile

23% of scientists in this discipline score the same or lower.

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

Overview

Ulisses M. Braga-Neto is affiliated with Texas A&M University in the United States. Their research spans multiple interconnected fields including Artificial Intelligence, Plant Science, Statistical and Nonlinear Physics, Molecular Biology, and Ecology. This multidisciplinary approach is reflected in their publication record and research topics.

The scientist has contributed to a variety of topics, notably in Smart Agriculture and AI, Model Reduction and Neural Networks, Neural Networks and Applications, Remote Sensing in Agriculture, Insect and Arachnid Ecology and Behavior, Innovations in Aquaponics and Hydroponics Systems, and Water Quality Monitoring Technologies.

Braga-Neto's recent papers include:

  • Self-adaptive physics-informed neural networks, 2022, Journal of Computational Physics
  • Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism, 2020, arXiv (Cornell University)
  • Self-Adaptive Physics-Informed Neural Networks, 2022, SSRN Electronic Journal
  • Nutrient optimization for plant growth in Aquaponic irrigation using Machine Learning for small training datasets, 2022, Artificial Intelligence in Agriculture
  • A Machine-Learning-Based IoT System for Optimizing Nutrient Supply in Commercial Aquaponic Operations, 2022, Sensors

The scientist frequently publishes in venues such as arXiv (Cornell University), IEEE Signal Processing Magazine, SSRN Electronic Journal, Artificial Intelligence in Agriculture, and Remote Sensing.

Braga-Neto has collaborated regularly with several co-authors who have each contributed to multiple publications alongside them. These frequent collaborators include:

  • Pappu Kumar Yadav
  • J. Alex Thomasson
  • Robert G. Hardin
  • Stephen W. Searcy
  • Sorin C. Popescu

Best Publications

  • Is cross-validation valid for small-sample microarray classification?

    Ulisses M. Braga-Neto;Edward R. Dougherty

  • Automatic target detection and tracking in forward-looking infrared image sequences using morphological connected operators

    Ulisses M. Braga-Neto;Ulisses M. Braga-Neto;Manish Choudhary;John Goutsias

  • Topology correction in brain cortex segmentation using a multiscale, graph-based algorithm

    Xiao Han;Chenyang Xu;U. Braga-Neto;J.L. Prince

  • Evaluation of the coverage and depth of transcriptome by RNA-Seq in chickens

    Ying Wang;Noushin Ghaffari;Charles D. Johnson;Ulisses M Braga-Neto

  • Bolstered error estimation

    Ulisses M. Braga-Neto;Ulisses M. Braga-Neto;Edward R. Dougherty;Edward R. Dougherty

  • Is cross-validation better than resubstitution for ranking genes?

    Ulisses Braga-Neto;Ronaldo Hashimoto;Edward R. Dougherty;Danh V. Nguyen

  • Maximum-Likelihood Adaptive Filter for Partially Observed Boolean Dynamical Systems

    Mahdi Imani;Ulisses M. Braga-Neto

  • A Theoretical Tour of Connectivity in Image Processing and Analysis

    Ulisses Braga-Neto;John Goutsias

  • From functional genomics to functional immunomics: new challenges, old problems, big rewards.

    Ulisses M Braga-Neto;Ernesto T. A Marques

  • Connectivity on complete lattices: new results

    Ulisses Braga-Neto;John Goutsias

  • Particle filters for partially-observed Boolean dynamical systems

    Mahdi Imani;Ulisses M. Braga-Neto

  • Bayesian Control of Large MDPs with Unknown Dynamics in Data-Poor Environments

    Mahdi Imani;Seyede Fatemeh Ghoreishi;Ulisses M. Braga-Neto

  • EPISTEMOLOGY OF COMPUTATIONAL BIOLOGY: MATHEMATICAL MODELS AND EXPERIMENTAL PREDICTION AS THE BASIS OF THEIR VALIDITY

    Edward R. Dougherty;Edward R. Dougherty;Ulisses Braga-Neto

  • MFBO-SSM: Multi-Fidelity Bayesian Optimization for Fast Inference in State-Space Models

    Mahdi Imani;Seyede Fatemeh Ghoreishi;Douglas L. Allaire;Ulisses M. Braga-Neto

  • Exact performance of error estimators for discrete classifiers

    Ulisses Braga-Neto;Edward Dougherty

  • Fads and fallacies in the name of small-sample microarray classification - A highlight of misunderstanding and erroneous usage in the applications of genomic signal processing

    U. Braga-Neto

  • Severe Dengue Prognosis Using Human Genome Data and Machine Learning

    Caio Davi;Andre Pastor;Thiego Oliveira;Fernando B. de Lima Neto

  • Impact of error estimation on feature selection

    Chao Sima;Sanju Attoor;Ulisses Brag-Neto;James Lowey

  • Reliable Classifier to Differentiate Primary and Secondary Acute Dengue Infection Based on IgG ELISA

    Marli Tenório Cordeiro;Ulisses Braga-Neto;Ulisses Braga-Neto;Rita Maria Ribeiro Nogueira;Ernesto T. A. Marques;Ernesto T. A. Marques

  • Grayscale level connectivity: theory and applications

    U. Braga-Neto;J. Goutsias

  • Performance of Error Estimators for Classification

    Edward R. Dougherty;Chao Sima;Hua;Blaise Hanczar

  • Control of Gene Regulatory Networks Using Bayesian Inverse Reinforcement Learning

    Mahdi Imani;Ulisses M. Braga-Neto

Frequent Co-Authors

Edward R. Dougherty
Edward R. Dougherty Texas A&M University
Ernesto T. A. Marques
Ernesto T. A. Marques University of Pittsburgh
John Goutsias
John Goutsias Johns Hopkins University
Marli Tenório Cordeiro
Marli Tenório Cordeiro Oswaldo Cruz Foundation
Huaijun Zhou
Huaijun Zhou University of California, Davis
Sergio Crovella
Sergio Crovella Qatar University
Luis O. Tedeschi
Luis O. Tedeschi Texas A&M University
Raymundo Arroyave
Raymundo Arroyave Texas A&M University
Gary L. Williams
Gary L. Williams Texas A&M University
Duane H. Keisler
Duane H. Keisler University of Missouri

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