About

Abel Rodriguez is a Professor of Statistics at the University of Washington, where he also serves as the Chair of the Department of Statistics. He is an affiliate member of the eScience Institute and the Center for Statistics in the Social Sciences. His research involves developing statistical methods for complex problems across biology, social sciences, and engineering. His interests include Bayesian statistics and machine learning, with a focus on nonparametric methods, spatio-temporal models, network analysis, and extreme value theory.

Research topics

• Computer Science
• Machine Learning
• Artificial Intelligence
• Statistics
• Mathematics
• Econometrics
• Neuroscience
• Psychology

Selected publications

• Computational Strategies and Estimation Performance With Bayesian Semiparametric Item Response Theory Models

  Journal of Educational and Behavioral Statistics · 2022 · 15 citations

  • Computer Science
  • Machine Learning
  • Computer Science

  Item response theory (IRT) models typically rely on a normality assumption for subject-specific latent traits, which is often unrealistic in practice. Semiparametric extensions based on Dirichlet process mixtures (DPMs) offer a more flexible representation of the unknown distribution of the latent trait. However, the use of such models in the IRT literature has been extremely limited, in good part because of the lack of comprehensive studies and accessible software tools. This article provides guidance for practitioners on semiparametric IRT models and their implementation. In particular, we rely on NIMBLE, a flexible software system for hierarchical models that enables the use of DPMs. We highlight efficient sampling strategies for model estimation and compare inferential results under parametric and semiparametric models.

  PublisherDOI

• Bayesian Regression With Undirected Network Predictors With an Application to Brain Connectome Data

  Journal of the American Statistical Association · 2020 · 24 citations

  Senior authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Machine Learning

  This article focuses on the relationship between a measure of creativity and the human brain network for subjects in a brain connectome dataset obtained using a diffusion weighted magnetic resonance imaging procedure. We identify brain regions and interconnections that have a significant effect on creativity. Brain networks are often expressed in terms of symmetric adjacency matrices, with row and column indices of the matrix representing the regions of interest (ROI), and a cell entry signifying the estimated number of fiber bundles connecting the corresponding row and column ROIs. Current statistical practices for regression analysis with the brain network as the predictor and the measure of creativity as the response typically vectorize the network predictor matrices prior to any analysis, thus failing to account for the important structural information in the network. This results in poor inferential and predictive performance in presence of small sample sizes. To answer the scientific questions discussed above, we develop a flexible Bayesian framework that avoids reshaping the network predictor matrix, draws inference on brain ROIs and interconnections significantly related to creativity, and enables accurate prediction of creativity from a brain network. A novel class of network shrinkage priors for the coefficient corresponding to the network predictor is proposed to achieve these goals simultaneously. The Bayesian framework allows characterization of uncertainty in the findings. Empirical results in simulation studies illustrate substantial inferential and predictive gains of the proposed framework in comparison with the ordinary high-dimensional Bayesian shrinkage priors and penalized optimization schemes. Our framework yields new insights into the relationship of brain regions with creativity, also providing the uncertainty associated with the scientific findings. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

  DOI

Recent grants

• Algorithms for Threat Detection: Detection and management of emerging diseases

  NSF · $319k · 2009–2014

• ATD: Relational Point Process Models: Theory, Methods, and Applications

  NSF · $455k · 2020–2025

• NIH Grant R01GM090201

  NIH · $1.4M · 2015

• ATD: A Novel Statistical Framework for Sensor Fusion

  NSF · $636k · 2013–2018

• ATD: Relational Point Process Models: Theory, Methods, and Applications

  NSF · $455k · 2020–2021

Frequent coauthors

• Chelsea Lofland

  University of California, Santa Cruz

  251 shared

• Scott Moser

  251 shared

• David B. Dunson

  37 shared

• Alan E. Gelfand

  Duke University

  19 shared

• Enrique ter Horst

  Universidad de Los Andes

  13 shared

• Antonio Lijoi

  11 shared

• Igor Prünster

  11 shared

• Adrian Dobra

  University of Washington

  10 shared

Similar researchers at University of Washington

• Sarah Jaewon Leeh-151
• Jan Hansenh-130
• Linda Simonsenh-120
• Anna Waldh-119
• Jesse Bloomh-117