cfDNA-Scientist: Multi-Agent Biomedical Feature Discovery
Jun 1, 2026
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1 min read
Role: Project Lead · Northwestern University (Remote) · Jun 2026 – Oct 2026
cfDNA-Scientist is a self-evolving multi-agent system that autonomously proposes, implements, and evaluates biomedical feature-extraction code in executable research environments, using cell-free DNA data.
What I did
- Built the multi-agent system. Agents coordinate hypothesis generation, feature implementation, verification, critique, and curation, running the generated code in executable research environments.
- Designed a literature-search workflow. A preliminary workflow identifies established cfDNA fragmentomic features, builds a feature catalog, and validates baseline performance.
- Evaluated on breast cancer detection. cfDNA-Scientist reached a cross-validated AUC of 0.916, compared with 0.831 for the baseline under the same evaluation protocol.
See the research overview for how this fits into my broader work.
Authors
M.S. Student in Electrical and Computer Engineering
I am an M.S. student in Electrical and Computer Engineering at UC San Diego, with a B.S. in Electrical Engineering from National Taiwan University. I build AI agents and reinforcement learning methods for scientific discovery, including multi-agent systems for biomedical feature discovery and policy optimization for discrete generative models, and I previously worked as an LLM engineer intern at the Inventec Artificial Intelligence Center.