I build machine learning for medical imaging and computational biology, and I care about making the models fair, robust, and useful where it counts.
Open to research and industry roles in bioinformatics and ML.
I'm a computer science master's student at Dartmouth, working in Dr. Saeed Hassanpour's lab on machine learning for digital pathology.
My path into this field began in precision oncology. As an undergraduate at Adelphi I built deep learning tools for proton therapy at the New York Proton Center, where I saw how much careful modeling matters when the output guides a patient's treatment.
Fine-tuning foundation models on whole-slide images to read disease straight from tissue morphology.
Deep learning pipelines for CT and ultrasound that support real treatment planning and screening.
Auditing whether medical models learn true pathology or dataset shortcuts across skin tones and sites.
A fully funded, year-long fellowship for bioinformatics and imageomics research, including on-site field work in Hawaii that applies machine learning to environmental questions.
Fine-tune the GigaPath foundation model on whole-slide histopathology to classify autoimmune and cholestatic liver disease, and co-lead a YOLOv8 ultrasound system for cervical cancer screening in low-resource settings.
Built CT analysis tools that generate stopping-power maps under 1% error to support proton treatment planning, and benchmarked dual-energy against single-energy models.
Shipped a GPT-powered document search system that cut support tickets by 50%, and built Python ETL pipelines and Dockerized cross-compilation builds hosted on AWS.