Research Thread
Computational Phenotyping
Finding hidden cognitive and clinical structure in complex human data.
This thread focuses on discovering interpretable patterns in heterogeneous cognitive, behavioral, clinical, and biomarker data. The goal is to move beyond average effects and toward profiles that can generate hypotheses, clarify mechanisms, and support more precise translational questions.
How can high-dimensional cognitive and clinical data reveal meaningful subgroups rather than average effects?
Identifying and distinguishing cognitive profiles among virally suppressed people with HIV.
Neuropsychology
Identifies six cognitive profiles among virally suppressed people with HIV and the factors that distinguish them.
Machine learning approaches to understand cognitive phenotypes in people with HIV
The Journal of infectious diseases
Frames machine learning as a tool for discovering cognitive biotypes in people with HIV.
Biopsychosocial phenotypes in people with HIV in the CHARTER cohort
Brain Communications
This paper helps define a research thread in computational phenotyping and cognitive subgroup discovery, providing context for how computational and translational evidence can be organized into reusable scientific systems.
Patterns and predictors of cognitive function among virally suppressed women with HIV
Frontiers in neurology
Uses self-organizing maps and random forests to characterize cognitive profiles in virally suppressed women with HIV.