I conducted biomedical data science research at Case Western Reserve University's Biomedical Engineering Department, working under Dr. Viswanath to develop statistical models for assessing treatment outcomes in rectal cancer patients. I independently analyzed multi-dimensional MRI and clinical data containing hundreds of imaging and clinical variables, employing and comparing various regression techniques including linear, lasso, and ridge regression to identify the most significant predictive variables for cancer tumor grade levels. Using Python (with NumPy, Matplotlib, Seaborn, Scikit-Learn, and Pandas libraries) and Stata, I developed models to predict colorectal cancer outcomes and presented my findings through a comprehensive research paper titled "Can we Predict Colorectal Cancer Outcomes using MRI Data? A Comparative Analysis of Different Techniques." My research earned recognition at multiple science fairs across Ohio, including the Regeneron Biomedical Science Award at the Buckeye Science & Engineering Fair, 2nd Place in Health Sciences at the Northeast Ohio Science & Engineering Fair, and a Statistical Analysis Award at State Science Day. Building on this foundation, I'm now extending this work into quantum machine learning through the MathQuantum Fellowship, where I'm developing both classical and quantum machine learning models under the guidance of mentors from CWRU and the University of Maryland to predict rectal cancer recurrence. This research will be published and presented at the MathQuantum Annual Symposium in February 2026.