💡 My Passion & What Drives Me

I am a Ph.D. student at the School of Public Health, Xiamen University (since Sept 2026), specializing in infectious disease forecasting, spatiotemporal dynamics, and viral evolution, with an M.Sc. in Computer Science and Technology from the joint training programme of Macau University of Science and Technology (Faculty of Innovation Engineering) and Xiamen University (Supervisor Prof. Chitin Hon; Lab director Academician Nanshan Zhong; Xiamen University supervisor Prof. Tianmu Chen), and a B.Med. in Preventive Medicine and a second-degree B.Sc. in Mathematical Statistics (WISE) from Xiamen University. I am deeply passionate about bridging the gap between data science, machine learning, and epidemiology.

What drives me every day is the belief that by deciphering complex health data and building robust prediction models, we can provide earlier warnings for public health challenges. I am a strong advocate for Open Science and making research accessible to everyone.

🛠️ What I Do

Through my research and GitHub repositories, I work on translating complex epidemiological models, non-stationary time-series analyses, and multimodal forecasting algorithms into open-source, reproducible code.

Whether it's analyzing the co-circulation of influenza or modeling small-sample outbreaks, I strive to share my workflows and tools to help fellow researchers and developers in the community. Active research engineering also lives in epic-intel-harness (deterministic-safety outbreak-intelligence agent benchmark), hanta-scientific-data-resource (DVC + Snakemake ResearchOps for hantavirus / ebola), openscience (evidence-first research workflow harness with a science-epi skill), and mcodeforlegal (Mainland-China-first legal workflow plugins for Claude Code).

🌱 How Your Contributions Make a Difference

Balancing academic research and open-source development comes with its own set of challenges and costs. Your generous sponsorship will directly help with:

• Computing Resources: Covering server and GPU costs needed to train machine learning models and process large-scale epidemiological datasets.
• Research & Maintenance: Supporting data acquisition, publication costs, and the time required to continuously maintain and update my open-source repositories.
• Fueling the Late Nights: Buying me a cup of coffee during those long nights of debugging code and writing manuscripts.

🤝 Let's Connect

Science and open-source thrive on collaboration. Whether you've cited my papers, used my code, or simply share an interest in infectious disease modeling, I deeply appreciate your presence here.

Every contribution, no matter the size, reminds me that I am not working in isolation. Thank you for supporting my journey in advancing public health research through data and code.

Available support channels: WeChat Pay, Alipay, Stripe monthly support, and Stripe one-time support.

• MiniMax-AI/MiniMax-Code-Plugins: 6 plugins in the official MiniMax Code plugin registry: openscience, mcodeforlegal, open-protein-binder-design, minimax-token-plan, document-skills, cli-agent-bridge (Aug–Sep 2026).
• hetaoBackend/MiniMax-Code-Plugins: search-first, verification-loop, mcp-server-patterns plugins (Aug 2026).
• lidge-jun/opencodex#6578: fixed tests that interfered with each other and hung when child processes did not exit (Oct 2026).

• Dual-model framework for CHIKV transmission modeling: ODE and Petri Net Analysis of the 2025 Foshan outbreak. Co-first author (listed first). BMC Public Health 26 (2026). DOI 10.1186/s12889-026-28110-9.
• Spatiotemporal instability of influenza seasonality during viral co-circulation. Co-first author (listed first). npj Systems Biology and Applications 12 (2026). DOI 10.1038/s41540-026-00729-9.
• Dengue transmission dynamics in China’s border regions adjacent to Myanmar. Co-author (7/11). Infectious Diseases of Poverty (2026). DOI 10.1186/s40249-026-01440-x.
• MAESTRO (Multi-modal Adaptive Estimation for Temporal Respiratory Disease Outbreak). arXiv preprint arXiv:2509.08578 (2025); reports R² = 0.956 on an 11-year Hong Kong influenza dataset (excluding the COVID-19 period).