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 the structure learning and estimation of latent variable models, with a focus on nonlinear settings. This line of work develops modern machine learning methods (e.g. deep neural networks) 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