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.
Contact: wbc23@mails.tsinghua.edu.cn
- 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, scientific computing, PDEs, multiscale modeling, and homogenization theory.
- AI for Science: AI-assisted mathematical discovery, molecular/material design, and research automation.
- Quantitative Homogenization Theory for Lamé-Stokes Coupled Systems — single-author preprint.
- AI Mathematician as a Partner in Advancing Mathematical Discovery - A Case Study in Homogenization Theory — co-first author; ICAIS 2025 Outstanding Paper.
Co-first author of AI Mathematician as a Partner in Advancing Mathematical Discovery, an ICAIS 2025 Outstanding Paper.
- Focus: LLM-assisted proof exploration, human-AI collaboration, and mathematical discovery workflows.
- Link: arXiv:2510.26380
Working on a private AI-for-Trading research prototype with live-market validation and investor-facing exploration.
- Focus: event-driven market signals, news-to-signal extraction, structured research workflows, and realistic validation.
- Status: private prototype; implementation details and performance metrics are not publicly disclosed.
Earlier mathematical research focused on PDEs and quantitative homogenization theory, especially the Lamé-Stokes coupled system.
- Link: arXiv:2606.05098
- Topics: two-scale expansions, mixed variational methods, convergence-rate estimates, and corrector regularity.
- This work trained me in rigorous analysis, multiscale modeling, and proof-based problem solving.
I have also explored AI for Science problems related to molecular and material design, including OLED molecular design and MOF representation/generation.
- Ph.D. in Computational and Applied Mathematics, Qiuzhen College, Tsinghua University, 2026.09-2029.06
- M.S. in Mathematics, Qiuzhen College, Tsinghua University, 2023.09-2026.06
- B.S. in Mathematics and Applied Mathematics, Ningbo University, 2019.09-2023.06
- Programming: Python, C/C++
- LLM systems: Transformer-based models, autoregressive modeling, prompt workflows, tool use, retrieval, and structured extraction
- Quantitative research: event-driven signals, market data workflows, time-series modeling, backtesting concepts, and risk-aware evaluation
- Mathematics: numerical analysis, PDEs, multiscale modeling, probability, statistics, and scientific computing
- ICAIS 2025 Outstanding Paper Award
- Tsinghua Qiuzhen College Creative Paper Award, 2025
- Tsinghua University Graduate with Merit, 2026
- Chinese Mathematics Competitions, Final Round, Second Prize, 2023
- Mathematical Contest in Modeling, Honorable Mention
- Shenzhen Cup Mathematical Modeling Challenge, Third Prize
- Zhejiang Provincial Government Scholarship, 2020, 2021, 2022
Research internships and collaborations in quantitative research, LLM systems, computational mathematics, and AI for Science.