My Entire Research History:
1). Undergraduate/Graduate Research Assistant – Stony Brook Biofluids Research Group (2021-Current):
I first joined this lab led by Dr. Danny Bluestein in May of 2021, where I worked on a project to conduct wet lab experiments on collected blood samples to observe platelet flipping in shear flow conditions. The data we generated with this project was used to validate a multiscale-physics model of platelet dynamics in shear flow. It was here where I first really dove deep into the world of machine learning and image segmentation as I strived to improve our data processing and calculation methods as this project already leveraged image segmentation to collect spatial-temporal information of individual platelets flipping in shear flow.
As I gained more experience, I started my current project, which is to validate mesh-generating deep learning algorithms by comparing their mesh outputs to meshes created through our current manual segmentation means to see their fidelity and where the algorithms may improve so that we may use these algorithms in our TAVR simulation workflows. This project is split into two parts: geometric comparison and simulation comparison. The geometric comparison leverages several statistical geometry comparative methods and the simulation comparisons are conducted through finite element analysis (FEA) and fluid-solid interactions (FSI).
2). High School Research Assistant – Ellen Li Group at Stony Brook Medicine (2018-2020)
In high school, I had the exciting opportunity to be a part of Ellen Li’s clinical research group in the Gastroenterology Department at Stony Brook Medicine. It was here where I got my feet wet in creating my own project, analyzing my own data to generate conclusions, learning several assay techniques, and how to really critically think to solve problems.
My project quantified different changes in bacterial populations of the gut microbiota in recurrent C.Difficile infection (CDI) patients who were a part of clinical trial utilizing a fecal microbiota transplant to treat CDI. We didn’t know why there was such a high success rate with this treatment in curing this disease, but my project sought to find it through correlations. After a bile acid analysis of the patients, I found that there was a decreased amount of C.Diff inhibiting bile acids (and the bacteria that produce them) in infected patients which markedly increased in post-treatment and cured patients.