I'm a graduate researcher at Dartmouth in the Hassanpour Lab, where we build machine learning tools for digital pathology and medical imaging. Most of my time is spent in PyTorch, figuring out how to build diagnostic models that are actually useful—and ethical—in a real clinical setting.
Before Dartmouth, I got my CS degree with a Statistics minor at Adelphi. My senior thesis with the New York Proton Center focused on improving proton stopping power estimation for pediatric cancer therapy using dual-energy CT. That project really solidified my interest in the gap between raw data and actual patient care.
I value clean data pipelines, readable code, and models that handle uncertainty responsibly. When I'm not running experiments, you can usually find me hunting for good matcha, reading Dostoevsky, or heading out to the stables.
Frontend and analysis layer for a dual-energy CT pipeline built with the New York Proton Center. The tool lets medical physicists inspect reconstructions, compare stopping-power estimates, and feed the results back into treatment planning — without leaving the browser.
A ReAct-style AI coding assistant that lives in the terminal. Built with semantic RAG for codebase navigation, automated task planning, and strict file-permission guardrails—because AI tools should write code, not quietly overwrite your directories. Basically, a pocket-sized software engineer.
An audit of skin lesion classifiers to determine if they learn genuine pathology or merely exploit dataset artifacts like clinical markings and demographic features. By systematically removing confounders and applying GradCAM, this project exposes what models are actually looking at—ensuring they diagnose the condition, not the photo.