Hudson Golino’s research focuses on quantitative methods, psychometrics and machine learning applied in the fields of psychology, health and education. He is particularly interested in new ways to assess the number of dimensions (i.e. latent variables) underlying multivariate data using network psychometrics.
He has been developing a new set of quantitative techniques and metrics, integrated in a general approach – termed Exploratory Graph Analysis (EGA), that is part of the relatively new area of network psychometrics. Particularly, he combines network science, information and quantum information theory, as well as computational methods to address fundamental problems in psychometrics, with the following goals: (1) to improve the estimation of the number of latent factors in an automatic (or semi-automatic) way, (2) to develop innovative fit indices for structural analysis and dimensionality assessment/reduction, (3) to improve the estimation and the interpretability of latent factors in intensive longitudinal data, (4) to develop new techniques for item analysis from a network psychometrics perspective (including, for example, network loadings, item parameters and new metrics of reliability), and (5) to construct general representations of structure built from intraindividual variability, quantifying the homogeneity of individuals using new metrics of complexity.
In 2012 he was awarded with the International Test Commission Young Scholar Scholarship and in 2015 he received the Sanofi Innovation in Medical Services award for developing a system to improve the prediction accuracy of outcomes in intensive care units using machine learning models.
Golino completed his Ph.D. in March 2015 at the Universidade Federal de Minas Gerais (Brazil), where he studied applications of machine learning in Psychology, Education and Health...