My research applies multidisciplinary methods to scholarly big data. I aim to contextualize and elucidate the social dynamics of the global scientific ecosystem by leveraging massive datasets, computational techniques, and social theories. Specifically, my research focuses on three threads:
Knowledge integration, production, and diffusion. I adopt complex system-based quantifications or simulations (e.g., configuration models, percolations, and randomizations) to study the process of knowledge flow in various scenarios;
Evidence-based science policy. I apply causal inference methods (e.g., difference-in-differences, instrumental variables, and matching) on macro- or micro-scientific systems (e.g., funding allocation, interdisciplinarity, gender/race equity, and scientific collaboration) around the globe; and
Entities and behaviors in scholarly publishing, such as biological/AI entities and their relations, the inequality of scholars’ attention, etc. I apply machine learning/deep learning methods to decipher the actors and their engagements in the knowledge embedding space.