I am a PhD student in Medical Engineering and Medical Physics at the Harvard-MIT Program in Health Sciences and Technology, advised by Dr. Polina Golland. My work is supported by a Neuroimaging Training Program Grant from the National Institute of Biomedical Imaging and Bioengineering. Before starting my PhD, I completed my BS/MS in Biomedical Engineering at Johns Hopkins University, advised by Dr. Joshua Vogelstein and Dr. Carey Priebe.
The goal of my research is to address unmet clinical needs through the development of biomedical machine learning methods that deepen our ability to understand and treat disease. My current focus is making minimally invasive neurosurgery easier for clinicians and safer for patients by designing and implementing fast 3D computer vision algorithms (neural fields) that advance the standard of intraoperative image guidance.
My past research has focused on the development of computational methods to analyze multi-subject neuroimaging data. Previously, I developed statistical graph theory algorithms to perform biomarker discovery in network-valued maps of the brain. I also led a Design Team of undergraduate biomedical engineers to build a dynamic fusion image guidance system for minimally invasive heart surgery, where I formed my interest in the intersection of computer vision and medicine.