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Engineering and Technology

D-Index
69
Citations
22414
World Ranking
1107
National Ranking
214

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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