Jack joined the Ph.D. program at the Department of Political Science in 2023. With dense trainings in data science (especially neural networks), network science, causal inference (Rubin's and Pearl's approaches), and formal theory, his research is focused on (1) detecting and reconstructing latent structures in big data, (2) predictive modeling and dynamic modeling, and (3) making non-linear models interpretable.