Ruishi is a Ph.D. student in the Education Data Science program, advised by Prof. Daniel McFarland. Her research examines how evaluative processes shape knowledge diffusion and how emerging technologies transform these processes. In particular, she studies how institutions assess ideas and innovations using computational methods such as natural language processing, social network analysis, causal inference, and LLM-based simulation. Her current work focuses on peer review and patent examination as key sites of knowledge evaluation, as well as the adoption of AI-enabled tools in educational settings.