Hudson Golinoمشاهده پروفایل
دانشیار
Hudson Golino is an Associate Professor in the Department of Psychology at the University of Virginia, within the College of Arts and Sciences. His academic work bridges quantitative methods, psychometrics, and machine learning with applications across psychology, health, education, and political science. Research Interests: Dr. Golino specializes in quantitative psychology and psychometrics, with a focus on developing new methodologies for dimensionality assessment and structural analysis. He is a pioneer in Network Psychometrics , particularly through Exploratory Graph Analysis (EGA) , integrating network science, information theory, quantum information theory, and computational modeling. His research goals include improving latent factor estimation, developing innovative fit indices, enhancing interpretability in longitudinal data, and creating new metrics for item analysis and reliability. He also works on NLP, topic modeling, and text mining with applications in cybersecurity and political discourse. Recent Publications: His recent work demonstrates a strong trend in combining psychometric rigor with computational innovation. Articles focus on stability assessment via bootstrap EGA, network analysis of schizotypy, dynamic topic modeling of social media trolls, and assistive communication technologies. These reflect a consistent interdisciplinary approach spanning psychology, data science, and social informatics. Scientific Awards: International Test Commission Young Scholar Scholarship (2012) Sanofi Innovation in Medical Services Award (2015) – for improving ICU outcome prediction using machine learning Grants and Advising: Dr. Golino leads three major grant-funded initiatives at UVA focused on (1) formalizing and testing language model properties using quantum information theory, (2) developing software combining dynamic systems, NLP, and psychometrics for text modeling, and (3) creating a novel toolkit for dimensionality reduction across data types. While no formal students are listed, he actively seeks collaborators and mentors graduate and undergraduate students in quantitative methods and applied machine learning. Labs and Teams: Though no specific lab name is mentioned, his research group at UVA is clearly engaged in cutting-edge work in data science and psychometrics, likely operating as part of the Psychology Department's quantitative program. His team develops open scientific tools and software, contributing to open scholarship through platforms like The Open Scholar.
