My research interests lie in the intersection between latent variable modeling and statistical learning. Much of my research is motivated by concepts of fairness and interpretability in the social and behavioral sciences. Topics include:
- the structural learning of latent variable models,
- multivariate outlier detection,
- model-based clustering,
- algorithmic fairness in educational testing,
- change-point detection for latent factor models.
Students interested in pursuing a PhD in the intersection of latent variable modeling and statistical machine learning, especially with applications in the social and behavioral sciences, are very welcome to contact me.
