Hudson Golino headshot
HG

Hudson Fernandes Golino

Assistant Professor
Unit: College of Arts and Sciences
Department: Department of Psychology
Office location and address
485 McCormick Rd
Charlottesville, Virginia 22903
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.

PSYC 2559: New Course in Psychology
Credits: 1–4
This course provides the opportunity to offer a new topic in the subject area of psychology.
PSYC 3590: Research in Psychology
Credits: 2–3
An original experimental project is undertaken in which each student is responsible for the design and operation of the experiment. S/U grading. May be repeated for credit. Prerequisite: 14 credits of psychology and instructor permission.
PSYC 4559: New Course in Psychology
Credits: 3
This course provides the opportunity to offer a new topic in the subject area of psychology.
PSYC 5559: New Course in Psychology
Credits: 3
This course provides the opportunity to offer a new topic in the subject area of psychology.
PSYC 5710: Machine Learning and Data Mining
Credits: 3
Machine learning and data mining are among the topics that are very demanded nowadays. They can be used to extract knowledge from multivariate datasets, to transform unstructured data into analyzable datasets, and to make extremely accurate and stable predictions. The present course will be an introductory, hands-on course, covering a number of basic techniques and methods used in the fields of machine learning and data mining, using R.
PSYC 7507: Contemporary Issues: Quantitative Psychology
Credits: 2
Discusses contemporary developments in psychological theory, methods, and research. Prerequisite: Graduate standing in psychology or instructor permission.
PSYC 7710: Quantitative Methods I: Probability and Statistical Inference
Credits: 4
The course covers mathematical foundations of psychology and statistical techniques used in behavioral science, in particular foundations of linear algebra, probability theory, information theory, statistical testing, normal models, and special, frequently used cases of normal models (t-test). The course has three lecture hours and two laboratory hours that teaches computational aspects of the course in R.
PSYC 8998: Non-Topical Research, Preparation for Thesis
Credits: 1–12
For master's research, taken before a thesis director has been selected.
PSYC 9501: Topical Research
Credits: 1–12
Independent laboratory research undertaken with advisor. Satisfactory/Unsatisfactory and can be repeated. Instructor permission required.
PSYC 9502: Topical Research
Credits: 1–12
Independent laboratory research undertaken with advisor. Graded and can be repeated. Instructor permission required.
PSYC 9998: Non-Topical Research, Preparation for Doctoral Research
Credits: 1–12
For doctoral research, taken before a dissertation director has been selected.
PSYC 9999: Non-Topical Research
Credits: 1–12
For doctoral dissertation, taken under the supervision of a dissertation director.