Hudson Golino

Assistant Professor of Psychology


Biography

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. Golino is also interested in identifying stage-like cognitive development, and in the development and validation of assessment instruments (e.g. tests and questionnaires).

Hudson Golino is the leading author of the first book written in Portuguese about the Rasch models (published by Pearson in Brazil in 2015). 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. 

Golino also holds an M.Sci. in Developmental Psychology (2012), an B.Sci. in Psychology (2011), all from Universidade Federal de Minas Gerais. At UVA, he will teach undergraduate and graduate courses on quantitative methods at the Department of Psychology. He expects to offer courses on applied machine learning for Psychologists and on the construction and validation of assessment instruments.

In the last couple of years, Golino has proposed a new approach, termed Exploratory Graph Analysis, that presents several advantages compared to traditional techniques used to verify the number of latent variables. At UVA, Golino will continue his Exploratory Graph Analysis project, and extend it to deal with intensive longitudinal data, which may contribute, for example, to the understanding of (1) human development, (2) the dynamics of symptoms in psychopathology, and (3) the performance of students in educational tests over time.