Summer 2025
Youth Science Program
University of Miami
Response to chemotherapy against Prostate cancer is often challenging to predict, and can adversely impact the outcome. Predicting the responsiveness a priori can lead to better clinical outcomes. Using RNASeq data from individuals responsive or resistant to chemotherapy, I developed ML methods to identify features that predict the outcome of drug treatment. These methods can directly benefit the patients and clinicians deciding chemotherapy regimens.
Summer 2024
Science Internship Program UC Santa Cruz
Analyzed the applications of convolutional neural networks (CNNs) and how they categorize tumors based on cell morphology. Explored RNNs’ applications for addressing CNN’s lack of precision for complex tasks.