Dr. Li is a behavioral statistician with a joint appointment in the Department of Psychiatry & Behavioral Sciences and the Department of Epidemiology and Biostatistics. His expertise is in Bayesian Item Response Theory and Bayesian Multilevel Modeling. His current research focuses on Bayesian Nonparametric methods in psychology as well as Patient-Reported Outcomes data analysis using Machine Learning analytics, including Latent Dirichlet Allocation for free-text entries of PRO data. These methods offer new possibilities in PRO data analysis, e.g., in quantifying how cancer patients’ goals and priorities change over time using verbatim free-text transcripts of what patients say matter the most to them personally. Patients’ goals can now be incorporated to enhance conventional data from fixed-length QOL measures. Dr. Li also works on making these revolutionary ideas accessible to behavioral scientists in his tutorials on Bayesian psychometric methods (PMID: 29424559, 22362655, 27193366) and Bayesian nonparametric mixture modeling (forthcoming).