I am an assistant professor of Statistics at the University of Michigan, and a faculty affiliate of the Michigan Institute for Data Science (MIDAS).

Previously, I was a postdoctoral researcher with Professor Michael Jordan at the University of California, Berkeley. I completed Ph.D. in Statistics at Columbia University in 2020, advised by Professor David Blei, and B.Sc. in mathematics and computer science at the Hong Kong University of Science and Technology in 2014.

I work in the fields of Bayesian statistics, machine learning, and causal inference. My research interests include

• Bayesian statistics: Robust Bayesian inference, statistical and computational theory of variational Bayes, Bayesian model evaluation
• Representation learning: Causal perspectives of representation learning, identifiable representation learning (e.g. with interventions or sparsity)
• Probabilistic modeling: Latent point-process models, statistical aspects of generative models, empirical Bayes for generative models
• Causal inference: Valid statistical inference, computational approaches (e.g. partial identification and semiparametric estimation), latent-variable models
• Applications: Recommender systems and feedback loops, computational biology, text analysis, electronic health records, strategic interactions

During the semester, I hold weekly office hours in person and over zoom.