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 Dirichlet Process Mixture Modeling (PMID: 31217637) on psychological symptom clusters for patients with advanced/terminal cancer near the end of life and Latent Dirichlet Allocation to score patients’ quality of life (PMID: 30798421). Clinicians are no longer restricted by conventional QOL surveys which rely on fixed (and often lengthy) surveys that routinely neglect each patient’s idiosyncratic goals and priorities. 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).