Open to roles in the US, Singapore and Korea
Regulatory judgment, built into AI you can check.
Four years in drug regulatory affairs taught me to ask what evidence a claim needs. Now I build health AI and data pipelines with that question designed in.
Regulatory affairs, Chong Kun Dang
Global RA team. Biosimilars and novel drugs.
MS Health Data Science, UCSF
Epidemiology and Biostatistics
AI evaluation, UCSF Innovation Ventures
Success criteria for clinical decision-support AI.
Multi-omics research, UCSF
Co-first author, kidney transplant rejection.
Selected projects
Five to start with. All 16 projects, grouped by area
Rare Disease Phenotype Agent
Only the first step uses an LLM, and it cannot name a disease. Everything after is lookup against curated data, so the ranking cannot be made up.
Prompt2Pump
Compares candidate drugs to exercise-mimicking signals using molecular embeddings and LINCS gene-set matching.
Kidney transplant multi-omics
Brought four to five institutions together and integrated CyTOF, metabolomics and clinical data from kidney transplant patients. Findings are confidential pending a patent application.
GLP1-Sentinel
A signal detection pipeline over 20 years of raw FDA adverse event reports that reproduces known GLP-1 safety signals.
FDA Pathway Intelligence
Scores 510(k), De Novo and PMA pathways from public FDA data, ranks predicate devices, and explains its evidence and uncertainty.
For recruiters
Pick the track closest to your role. Each has three projects to read first and a résumé written for it.
Health Data Science & Bioinformatics
Health data scientist, bioinformatics scientist, computational biology
Download résumé PDF