Jing Liu is the Director of the Center for Educational Data Science and Innovation and an Assistant Professor in Education Policy at the University of Maryland College Park. Named as a National Academy of Education Sciences/Spencer Dissertation Fellow, he earned his Ph.D. in Economics of Education from Stanford University in 2018. Before he joined UMD, he was a Postdoctoral Research Associate at Brown University’s Annenberg Institute. Dr. Liu's research uses rigorous quantitative evidence to evaluate and inform education policies at the national, state, and local levels, with the goal of improving learning opportunities for historically marginalized students in urban areas. His work broadly engages with critical policy issues including student absenteeism, exclusionary discipline, educator’s labor market, school reform, and higher education. Grounded in economic theory and policy analysis, he uses both quasi-experimental designs and data science methods such as computational linguistic analysis to analyze large administrative data and unstructured information. Most of his current projects focus on understanding the development of student engagement, behavior, and social-emotional skills, how these skills and dispositions contribute to student success in the short and long run, and what the implications are for improving equal educational opportunities. His work has appeared in peer-reviewed journals such as the Journal of Public Economics, Journal of Human Resources, Journal of Policy Analysis and Management,  and Educational Evaluation and Policy Analysis. Please visit his personal website or Google scholar page for his most recent publications and projects.

Watch Dr. Liu's EdTalk entitled "Harnessing the Power of Artificial Intelligence to Support Teachers".

Watch a video by AERA in which Dr. Liu shared his findings from "Troublemakers? The Role of Frequent Teacher Referrers in Expanding Racial Disciplinary Disproportionalities", published by Educational Researcher