Ph.D. student in Computational and Applied Mathematics at Qiuzhen College, Tsinghua University (2026.09-2029.06).

I work at the intersection of Transformer-based large language models, quantitative research, computational mathematics, and AI for Science. My current interests include autoregressive modeling, LLM agents, event-driven market signals, and research workflow automation.

• Transformer-based and autoregressive models: large language models, tool use, retrieval, agents, and structured reasoning workflows.
• LLM-assisted quantitative research: event-driven signal extraction, news-to-signal pipelines, time-series modeling, and risk-aware validation.
• Computational mathematics: numerical methods, PDEs, multiscale modeling, homogenization theory, and scientific computing.
• AI for Science: AI-assisted mathematical discovery, molecular/material design, and research automation.

• AI Mathematician as a Partner in Advancing Mathematical Discovery - A Case Study in Homogenization Theory, ICAIS 2025 Outstanding Paper.
• Quantitative Homogenization Theory for Lamé-Stokes Coupled Systems, single-author preprint.
• Private AI-for-Trading research prototype, focused on event-driven market signals, structured information extraction, and live-market validation.

• Academic profile: academic-profile
• Email: wbc23@mails.tsinghua.edu.cn
• GitHub: Ms-March7

Research internships and collaborations in quantitative research, LLM systems, computational mathematics, and AI for Science.