Understanding how different nanoparticles interact with biological tissues is essential for developing safe and effective nanomedicines. In our lab, we use organ-on-a-chip models that mimic the human placenta and other organs to study how nanoparticles behave under realistic physiological conditions. This project will apply machine learning to experimental data collected from these models to predict cellular responses to nanoparticles based on their properties and exposure conditions.

The student will work with a graduate student to clean, organize, and analyze datasets, build predictive models, and create clear visualizations to interpret findings. This project offers an exciting opportunity to apply data science skills to a real-world biomedical challenge at the intersection of nanotechnology, microfluidics, and computational modeling.