World's Best Scientists 2026 revealed!
Marina Vannucci

Marina Vannucci

D-Index & Metrics

Computer Science

D-Index
41
Citations
7718
World Ranking
8794
National Ranking
3759

Mathematics

D-Index
39
Citations
7181
World Ranking
2172
National Ranking
917

Research.com Recognitions

  • 2012 - Fellow of the American Association for the Advancement of Science (AAAS)
  • 2006 - Fellow of the American Statistical Association (ASA)

Overview

Marina Vannucci is affiliated with Rice University in the United States and specializes primarily in the field of Computer Science. Their research covers a broad range of subfields, including Artificial Intelligence, Molecular Biology, Cognitive Neuroscience, Statistics and Probability, and Signal Processing.

The main topics of their work focus on Bayesian Methods and Mixture Models, Statistical Methods and Inference, Neural Dynamics and Brain Function, Gut Microbiota and Health, Metabolomics and Mass Spectrometry Studies, Statistical Methods and Bayesian Inference, and Functional Brain Connectivity Studies.

Marina Vannucci has published extensively, with notable recent papers including:

  • Bayesian statistics and modelling, 2021, Nature Reviews Methods Primers
  • Bayesian statistics and modelling, 2021, Nature Reviews Methods Primers
  • Bayesian graphical models for modern biological applications, 2021, Statistical Methods & Applications
  • Evidence of state-dependence in the effectiveness of responsive neurostimulation for seizure modulation, 2021, Brain Stimulation
  • Noradrenaline tracks emotional modulation of attention in human amygdala, 2023, Current Biology

Frequent co-authors they have collaborated with include Michele Guindani, Matthew D. Koslovsky, Beniamino Hadj-Amar, Meng Li, and Christine B. Peterson.

Their work has appeared in publication venues such as arXiv (Cornell University), Biometrics, The Annals of Applied Statistics, Bayesian Analysis, and Nature Reviews Methods Primers.

Marina Vannucci has received recognition in the form of fellowships. They were named a Fellow of the American Association for the Advancement of Science (AAAS) in 2012 and a Fellow of the American Statistical Association (ASA) in 2006.

Best Publications

  • Bayesian statistics and modelling

    Rens van de Schoot;Sarah Depaoli;Ruth King;Ruth King;Bianca Kramer

  • Multivariate Bayesian variable selection and prediction

    Philip J. Brown;Marina Vannucci;T. Fearn

  • Gene selection: a Bayesian variable selection approach

    Kyeong Eun Lee;Naijun Sha;Edward R. Dougherty;Marina Vannucci

  • Bayesian Variable Selection in Clustering High-Dimensional Data

    Mahlet G Tadesse;Naijun Sha;Marina Vannucci

  • A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data.

    Qianxing Mo;Ronglai Shen;Cui Guo;Marina Vannucci

  • Variable selection in clustering via Dirichlet process mixture models

    Sinae Kim;Mahlet G. Tadesse;Marina Vannucci

  • Bayesian Inference of Multiple Gaussian Graphical Models

    Christine B. Peterson;Francesco C. Stingo;Marina Vannucci

  • Bayesian wavelet regression on curves with application to a spectroscopic calibration problem

    Philip J. Brown;T. Fearn;Marina Vannucci

  • Bayes model averaging with selection of regressors

    Philip J. Brown;Marina Vannucci;T. Fearn

  • Incorporating biological information into linear models: A Bayesian approach to the selection of pathways and genes

    Francesco C. Stingo;Yian A. Chen;Mahlet G. Tadesse;Marina Vannucci

  • Bayesian variable selection in multinomial probit models to identify molecular signatures of disease stage.

    Naijun Sha;Marina Vannucci;Mahlet G. Tadesse;Philip J. Brown

  • Wavelet-Based Nonparametric Modeling of Hierarchical Functions in Colon Carcinogenesis

    Jeffrey S Morris;Marina Vannucci;Philip J Brown;Raymond J Carroll

  • Detecting Traffic Anomalies through Aggregate Analysis of Packet Header Data

    Seong Soo Kim;A. L. Narasimha Reddy;Marina Vannucci

  • Variable Selection for Nonparametric Gaussian Process Priors: Models and Computational Strategies

    Terrance Dean Savitsky;Marina Vannucci;Naijun Sha

  • Bayesian variable selection for the analysis of microarray data with censored outcomes

    Naijun Sha;Mahlet G. Tadesse;Marina Vannucci

  • Bayesian inference for gene expression and proteomics

    Kim-Anh Do;Peter Müller;Marina Vannucci

  • Covariance structure of wavelet coefficients: theory and models in a Bayesian perspective

    M. Vannucci;F. Corradi

  • Wavelet change-point prediction of transmembrane proteins.

    Pietro Lio;Marina Vannucci

  • Finding pathogenicity islands and gene transfer events in genome data.

    Pietro Liò;Marina Vannucci

  • The choice of variables in multivariate regression: a non-conjugate Bayesian decision theory approach

    Philip J. Brown;T. Fearn;Marina Vannucci

Frequent Co-Authors

Mingyu Liang
Mingyu Liang Medical College of Wisconsin
Allen W. Cowley
Allen W. Cowley Medical College of Wisconsin
Francesco Falciani
Francesco Falciani University of Liverpool
John M. Stern
John M. Stern University of California, Los Angeles
Raymond J. Carroll
Raymond J. Carroll Texas A&M University
Joanne R. Lupton
Joanne R. Lupton Texas A&M University
Kim Anh Do
Kim Anh Do The University of Texas MD Anderson Cancer Center
Robert S. Chapkin
Robert S. Chapkin Texas A&M University
Ching C. Lau
Ching C. Lau Baylor College of Medicine
Howard J. Jacob
Howard J. Jacob AbbVie (United States)

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