About
I am a cancer biologist now working at the intersection of patient-derived disease models and artificial intelligence. My career has centered on one consistent question: how do we capture the molecular and pharmacological heterogeneity of human cancers in tractable preclinical models — and turn that data into computational predictions of optimal treatment?
Over the past decade, I have worked extensively with patient-derived cancer organoids and cell lines spanning six major cancer types — colorectal, pancreatic, breast, lung, glioblastoma, and sarcoma. Through a combination of biobanking, multi-omics profiling, and drug response screening, my collaborators and I have established platforms that capture intratumoral heterogeneity, identify clinically actionable molecular subtypes, and probe drug sensitivity under perturbations ranging from microbial co-culture to simulated microgravity.
Since March 2025, as an Assistant Professor of Bio-Medical AI in the School of AI and Data Science at the University of Suwon, I am extending this body of work toward hit-to-lead optimization, computational drug efficacy screening, prediction of optimal treatment, AI-based multi-omics, and disease modeling — developing analytic and machine-learning frameworks that treat patient-derived models as living datasets.