Jasmine Y Foo

Jasmine Y Foo is a Professor in the School of Mathematics at the University of Minnesota, specializing in mathematical oncology with a focus on cancer mechanisms, drug resistance evolution, and tumor-stromal interactions. Her interdisciplinary work bridges mathematics, biology, and clinical oncology through computational modeling and data-driven approaches.

Her research centers on evolutionary dynamics in cancer, particularly non-genetic drug resistance mechanisms, tumor microenvironment interactions, and quantitative biomarker development. Key methodologies include statistical frameworks for therapy resistance detection, spatial modeling of mutant populations, and integrated imaging-mathematical models for tumor organoid systems. This work directly addresses challenges in treatment optimization and personalized cancer therapy.

Recent publications (2024-2025) reveal a concentrated effort on translating mathematical insights into clinical applications, with emphasis on circulating tumor DNA kinetics, stromal-induced resistance dosing strategies, and spatial models of cancer evolution. These studies consistently integrate high-throughput experimental data with novel computational approaches to predict treatment outcomes.

Dr. Foo maintains an active grants portfolio as Principal Investigator, including:

• National Science Foundation: Evolutionary dynamics of non-genetic drug resistance mechanisms (2021-2025)
• National Institutes of Health: Microengineered colon cancer-chip for tumor-stromal interaction studies (2021-2023)
• Rainwater Charitable Foundation: Progressive supranuclear palsy genetics-to-drug-discovery pipeline (2024-2025)
• Research Council of Norway: Norwegian-American alliance for data-driven cancer modeling (2020-2024)

Her research group operates at the mathematics-biology interface, collaborating with experimental oncologists and clinicians to develop predictive models using patient-derived organoids and tumor-chip systems. Current team efforts focus on creating dynamic biomarkers for treatment response and computational tools for resistance prevention in heterogeneous tumor populations.