World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
34
Citations
4269
World Ranking
12261
National Ranking
219

Overview

Luca Martino is affiliated with King Juan Carlos University in Spain and has contributed extensively to the field of computer science, focusing on areas such as artificial intelligence and statistics and probability. Their research spans across related subfields including statistics, probability and uncertainty, signal processing, and global and planetary change.

The scientist's work particularly emphasizes topics such as Gaussian processes and Bayesian inference, probabilistic and robust engineering design, and target tracking and data fusion in sensor networks. Additional research interests include Bayesian methods and mixture models, statistical methods and inference, Markov chains and Monte Carlo methods, as well as statistical methods and Bayesian inference.

Martino has published multiple research papers across various scientific venues. Some of their recent works include:

  • A Joint introduction to Gaussian Processes and Relevance Vector Machines with Connections to Kalman filtering and other Kernel Smoothers, 2020, arXiv (Cornell University)
  • A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data, 2021, Remote Sensing
  • Rethinking the Effective Sample Size, 2022, International Statistical Review
  • Marginal Likelihood Computation for Model Selection and Hypothesis Testing: An Extensive Review, 2023, SIAM Review
  • Importance Gaussian Quadrature, 2020, IEEE Transactions on Signal Processing

The scientist frequently collaborates with other researchers, including Fernando Llorente, David Delgado-Gómez, Víctor Elvira, Ernesto Curbelo, and Gustau Camps-Valls. These collaborations have resulted in numerous coauthored publications.

Luca Martino's research is published predominantly in venues such as arXiv (Cornell University), Mathematics, IEEE Transactions on Geoscience and Remote Sensing, Computational Statistics, and SSRN Electronic Journal. Their work reflects a broad engagement with both theoretical and applied aspects of computer science, with a focus on statistical and probabilistic methodologies.

Best Publications

  • Moving Fast with Software Verification

    Cristiano Calcagno;Dino Distefano;Jérémy Dubreil;Dominik Gabi

  • Adaptive Importance Sampling: The past, the present, and the future

    Monica F. Bugallo;Victor Elvira;Luca Martino;David Luengo

  • Effective sample size for importance sampling based on discrepancy measures

    Luca Martino;Víctor Elvira;Francisco Louzada

  • A survey of Monte Carlo methods for parameter estimation

    David Luengo;Luca Martino;Luca Martino;Mónica F. Bugallo;Victor Elvira

  • Cooperative parallel particle filters for online model selection and applications to urban mobility

    Luca Martino;Jesse Read;Jesse Read;Víctor Elvira;Francisco Louzada

  • Efficient monte carlo methods for multi-dimensional learning with classifier chains

    Jesse Read;Luca Martino;David Luengo

  • Independent Doubly Adaptive Rejection Metropolis Sampling Within Gibbs Sampling

    Luca Martino;Jesse Read;David Luengo

  • Generalized Multiple Importance Sampling

    Víctor Elvira;Luca Martino;David Luengo;Mónica F. Bugallo

  • Improving population Monte Carlo

    Víctor Elvira;Luca Martino;David Luengo;Mónica F. Bugallo

  • A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data

    Katja Berger;Juan Pablo Rivera Caicedo;Luca Martino;Matthias Wocher

  • An Adaptive Population Importance Sampler: Learning From Uncertainty

    Luca Martino;Victor Elvira;David Luengo;Jukka Corander

  • Layered adaptive importance sampling

    L. Martino;V. Elvira;D. Luengo;J. Corander

  • Scalable multi-output label prediction

    Jesse Read;Luca Martino;Pablo M. Olmos;David Luengo

  • Physics-aware Gaussian processes in remote sensing

    Gustau Camps-Valls;Luca Martino;Daniel H. Svendsen;Manuel Campos-Taberner

  • Orthogonal parallel MCMC methods for sampling and optimization

    Luca Martino;Víctor Elvira;David Luengo;Jukka Corander

  • Generalized Multiple Importance Sampling

    Víctor Elvira;Luca Martino;David Luengo;Mónica F. Bugallo

  • Marginal likelihood computation for model selection and hypothesis testing: an extensive review.

    Fernando Llorente;Luca Martino;David Delgado;Javier López-Santiago

  • Adaptive importance sampling in signal processing

    Mónica F. Bugallo;Luca Martino;Jukka Corander

  • A Review of Multiple Try MCMC Algorithms for Signal Processing

    Luca Martino;Luca Martino

  • Marginal Likelihood Computation for Model Selection and Hypothesis Testing: an Extensive Review

    F. Llorente;L. Martino;D. Delgado;J. Lopez-Santiago

  • Efficient Multiple Importance Sampling Estimators

    Victor Elvira;Luca Martino;David Luengo;Monica F. Bugallo

  • Group Importance Sampling for particle filtering and MCMC

    Luca Martino;Luca Martino;Víctor Elvira;Gustau Camps-Valls

  • A Joint introduction to Gaussian Processes and Relevance Vector Machines with Connections to Kalman filtering and other Kernel Smoothers

    Luca Martino;Jesse Read

  • Advances in Importance Sampling

    Víctor Elvira;Luca Martino

  • Layered Adaptive Importance Sampling

    L. Martino;V. Elvira;D. Luengo;J. Corander

  • Improving Population Monte Carlo: Alternative Weighting

    Luca Martino;David Luengo;F. Bugallo

Frequent Co-Authors

Jesse Read
Jesse Read École Polytechnique
Gustau Camps-Valls
Gustau Camps-Valls University of Valencia
Jukka Corander
Jukka Corander University of Oslo
Jordi Muñoz-Marí
Jordi Muñoz-Marí University of Valencia
Jochem Verrelst
Jochem Verrelst University of Valencia
Petar M. Djuric
Petar M. Djuric Stony Brook University
Luis Alonso
Luis Alonso Universitat Politècnica de Catalunya
Jose Moreno
Jose Moreno University of Valencia
Steven W. Running
Steven W. Running University of Montana
Simo Särkkä
Simo Särkkä Aalto University

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