Dr. Yiwang Li is a Postdoctoral Scholar on the California Teacher Pipeline Project at the University of California, Davis. He earned his Ph.D. in Education Policy Analysis and Leadership from the University of California, Riverside. His work sits at the intersection of education policy, large-scale administrative data, and advanced quantitative methods, with dissertation research focused on applying machine learning to predict college readiness.
Dr. Li’s research centers on three primary areas: the teacher workforce; college access, persistence, and attainment; and the application of machine learning methods in education research. Using administrative data, predictive modeling, and causal inference approaches, he seeks to identify at-risk students to support early intervention efforts, evaluate how policy changes shape educational outcomes, and better understand how student characteristics relate to long-term academic and workforce success. He has served as the quantitative lead on multiple major research grants funded by the NSF, IES, and the Haynes Foundation, and his work has been recognized with the Dean’s Distinguished Fellowship.
Committed to education across the P–20 continuum, Dr. Li has designed and taught courses in education policy analysis, educational research methods, and graduate-level statistical programming. Prior to his doctoral training, he taught at the elementary school level and worked as a data analyst in the Office of Teacher Effectiveness at the New York City Department of Education. Dr. Li holds an M.A. in Research, Evaluation, Measurement, and Statistics from UC Riverside and a B.S. in Education and Public Policy from Pennsylvania State University.