World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
69
Citations
22414
World Ranking
1108
National Ranking
213

Daniel P. Palomar publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Daniel P. Palomar sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 291 publications — 74th percentile

74% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Daniel P. Palomar D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Daniel P. Palomar sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 69 D-Index — 89th percentile

89% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Daniel P. Palomar is affiliated with the Hong Kong University of Science and Technology in China. Their research output spans multiple domains within computer science and mathematics, focusing on specialized fields such as statistics, probability, management science, operations research, artificial intelligence, finance, and signal processing.

The scientist's work covers several main topics including:

  • Advanced Statistical Methods and Models
  • Risk and Portfolio Optimization
  • Statistical Methods and Inference
  • Blind Source Separation Techniques
  • Financial Markets and Investment Strategies
  • Bayesian Modeling and Causal Inference
  • Stochastic Processes and Financial Applications

They have published extensively, with frequent contributions to venues such as arXiv (Cornell University), IEEE Transactions on Signal Processing, Signal Processing, TUbilio (Technical University of Darmstadt), and Nature Communications.

Recent papers authored by Daniel P. Palomar include:

  • "Majorization-Minimization on the Stiefel Manifold With Application to Robust Sparse PCA," 2021, IEEE Transactions on Signal Processing
  • "Seasonal antigenic prediction of influenza A H3N2 using machine learning," 2024, Nature Communications
  • "Student's $t$ VAR Modeling With Missing Data Via Stochastic EM and Gibbs Sampling," 2020, IEEE Transactions on Signal Processing
  • "Understanding the Quintile Portfolio," 2020, IEEE Transactions on Signal Processing
  • "Covariance Matrix Estimation Under Low-Rank Factor Model With Nonnegative Correlations," 2022, IEEE Transactions on Signal Processing

Collaboration is a significant aspect of their research, with frequent coauthors including Jiaxi Ying, Jasin Machkour, Michael Muma, José Vinícius de Miranda Cardoso, and Rui Zhou.

Daniel P. Palomar has authored a book titled Portfolio Optimization, published by Cambridge University Press in 2025.

Best Publications

  • A tutorial on decomposition methods for network utility maximization

    D.P. Palomar;Mung Chiang

  • Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning

    Ying Sun;Prabhu Babu;Daniel P. Palomar

  • Joint Tx-Rx beamforming design for multicarrier MIMO channels: a unified framework for convex optimization

    D.P. Palomar;J.M. Cioffi;M.A. Lagunas

  • Power Control By Geometric Programming

    M. Chiang;Chee Wei Tan;D.P. Palomar;D. O'Neill

  • Rank-Constrained Separable Semidefinite Programming With Applications to Optimal Beamforming

    Yongwei Huang;D.P. Palomar

  • Practical algorithms for a family of waterfilling solutions

    D.P. Palomar;J.R. Fonollosa

  • Demand-Side Management via Distributed Energy Generation and Storage Optimization

    I. Atzeni;L. G. Ordonez;G. Scutari;D. P. Palomar

  • Convex Optimization in Signal Processing and Communications

    Daniel P. Palomar;Yonina C. Eldar

  • Convex Optimization, Game Theory, and Variational Inequality Theory

    Gesualdo Scutari;Daniel Palomar;Francisco Facchinei;Jong-shi Pang

  • Gradient of mutual information in linear vector Gaussian channels

    D.P. Palomar;S. Verdu

  • Alternative Distributed Algorithms for Network Utility Maximization: Framework and Applications

    D.P. Palomar;Mung Chiang

  • Mimo Transceiver Design Via Majorization Theory

    Daniel P. Palomar;Yi Jiang

  • Optimization Methods for Designing Sequences With Low Autocorrelation Sidelobes

    Junxiao Song;Prabhu Babu;Daniel Pérez Palomar

  • Decomposition by Partial Linearization: Parallel Optimization of Multi-Agent Systems

    Gesualdo Scutari;Francisco Facchinei;Peiran Song;Daniel P. Palomar

  • A robust maximin approach for MIMO communications with imperfect channel state information based on convex optimization

    A. Pascual-Iserte;D.P. Palomar;A.I. Perez-Neira;M.A. Lagunas

  • Optimal Linear Precoding Strategies for Wideband Noncooperative Systems Based on Game Theory—Part I: Nash Equilibria

    G. Scutari;D.P. Palomar;S. Barbarossa

  • The MIMO Iterative Waterfilling Algorithm

    G. Scutari;D.P. Palomar;S. Barbarossa

  • Sequence Design to Minimize the Weighted Integrated and Peak Sidelobe Levels

    Junxiao Song;Prabhu Babu;Daniel P. Palomar

  • Competitive Design of Multiuser MIMO Systems Based on Game Theory: A Unified View

    G. Scutari;D. Palomar;S. Barbarossa

  • Statistically Robust Design of Linear MIMO Transceivers

    Xi Zhang;D.P. Palomar;B. Ottersten

  • Convex Optimization, Game Theory, and Variational Inequality Theory in Multiuser Communication Systems

    Daniel P. Palomar

  • Gradient of Mutual Information in Linear Vector

    Daniel P. Palomar;Sergio Verdú

Frequent Co-Authors

Gesualdo Scutari
Gesualdo Scutari Purdue University West Lafayette
Sergio Barbarossa
Sergio Barbarossa Sapienza University of Rome
Bjorn Ottersten
Bjorn Ottersten University of Luxembourg
Mung Chiang
Mung Chiang Purdue University West Lafayette
Francisco Facchinei
Francisco Facchinei Sapienza University of Rome
Jiaheng Wang
Jiaheng Wang Southeast University
Jong-Shi Pang
Jong-Shi Pang University of Southern California
John M. Cioffi
John M. Cioffi Stanford University
Yonina C. Eldar
Yonina C. Eldar Weizmann Institute of Science
Sergio Verdu
Sergio Verdu Princeton University

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