I have always been fascinated by the world of data analytics, specifically in biology. My other passion for coding drove me to discover machine learning. By combining my two passions, I found the realm of computational biology and bioengineering, where data science meets biology. I want to create a world where the rates of prevalent diseases can be decreased because of the interdisciplinary collaboration between people and machines.
My ultimate dream is to improve analysis and treatment of diseases through revolutionary improvements in biomedical technologies and computational analysis.
While interning at Arizona State University in the Health Systems Engineering Department, I am pursuing a project where I analyzed the correlation between MRI and molecular imaging. How are structure and function correlated in the adult mouse brain while keeping in mind the larger context of gene expression?
The goal of this project is to predict instances of Alzheimer's disease in patients after they have completed an MRI scan. By predicting who had Alzheimer's, we are able to save people by putting them into a preventative care treatment before they are even diagnosed with the disease.
By understanding EEG markers that correlate with cognitive decline in AD or PD, criteria to distinguish between MCI pathologies could be established. EEG has been increasingly used for diagnosing dementia because it is non-invasive and allows for continuous monitoring.
Alzheimer's Project: KNN classifier for best number of neighbors to implement in prediction algorithm
Gene Expression Project: Example rendering of Fbln1 gene expression in adult mouse brain