To develop safe nanoparticles for use during pregnancy, we first need to understand the cross-talk (communication) between cells of the placenta (barrier between the mother and the baby) and other cells from the mother at different pathological conditions, e.g. cancer. We developed an organ-on-a-chip model to mimic this environment in the lab and investigate the cross-talk between cells. We used this model to generate protemic and transcriptomic data. A data science student will work with a graduate student and help analyze this big data and enable different visualization approaches of the data. This a great opportunity for the student to work in an interdisciplinary team that works at the intersection between nanotechnology and microfluidics, and learn new wet-lab techniques, and apply their knowledge in data science to solve real-case problems.

To develop safe nanoparticles for use during pregnancy, we first need to understand the cross-talk (communication) between cells of the placenta (barrier between the mother and the baby) and other cells from the mother at different pathological conditions, e.g. cancer. We developed an organ-on-a-chip model to mimic this environment in the lab and investigate the cross-talk between cells. We used this model to generate protemic and transcriptomic data. A data science student will work with a graduate student and help analyze this big data and enable different visualization approaches of the data. This a great opportunity for the student to work in an interdisciplinary team that works at the intersection between nanotechnology and microfluidics, and learn new wet-lab techniques, and apply their knowledge in data science to solve real-case problems.

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.