I am an Assistant Professor (Fixed-Term) in the Department of Statistics & Data Science at UCLA. My research focuses on latent variable modeling with applications to factor analysis and missing data problems.
I am currently interested in structure learning for latent variable models, with a focus on non-linear settings. This line of work involves extending modern non-parametric estimation methods (e.g. deep neural networks, autoencoders) for factor analytic and missing data scenarios. These models pose unique computational challenges, often requiring specialized inference procedures based on Markov chain Monte Carlo and related stochastic algorithms.
I also have collaborative work on statistical inference, interpretation, and measurement problems in Quantitative Psychology.
Education
- Ph.D., Statistics, UCLA (2022)
- Ph.D., Quantitative Psychology, UCLA (2021)
- M.S., Statistics, UCLA (2020)
- M.A., Quantitative Psychology, UCLA (2019)
Research Interests
- Factor Analysis
- Missing Data
- Computational Statistics