Carnegie Mellon University (MS, METALS) · Peking University (BA, Sociology)
I am a Researcher and Data Scientist at Arizona State University, Learning Engineering Institute working with Dr. Danielle S. McNamara. I study the intersection of artificial intelligence and human learning. My ongoing research focuses on predictive student modeling and generative AI in education, particularly how large language models can be designed to adapt to individual learners and preserve student agency. Looking ahead, I am expanding my work toward more equitable and personalized learning systems that account for cultural and linguistic diversity, leverage real-time learning signals, and enhance students rather than replace their thinking.
Customizable voice simulation platform for professional onboarding, practice high-stakes workplace conversations before week 1
Multi-agent GenAI tutor for L2 French learning with LLM-based knowledge tracing, accepted at AIED 2026
First large-scale de-identified student discussion corpus with state-of-the-art PII removal, serving the Learning @ Scale grant
Adaptive educational games for K3–5 students with ESSA Tier 2 evidence of significantly higher math growth
Social bot's core natural language understanding section using NLTK, spacy, word2vec with Google and Microsoft API
2020 Tree Hack @ Stanford "Most Creative Hack" winning project, an indulging virtual reality drumming game
The first Peking University Student Social Network Model from over 2.8 million campus canteen log data