I am a Ph.D. student in the Department of Electrical Engineering at Stanford University. My research interests span the areas of signal processing, optimization, and artificial intelligence. I primarily work on developing fast, quantitative medical imaging methods with a strong focus in clinical translation. I am advised by Drs. Shreyas Vasanawala and Joseph Cheng. My research is funded by NSF GRFP.
Previously, I was an undergraduate at University of Southern California where I had the pleasure of working with Dr. Krishna Nayak. I've also done summer internships at NIH (2014) and GE Healthcare (2018).
Cardiac cine MRI is the gold standard for non-invasive assessment of heart function. However, the MRI data acquisition process is slow and must be performed over the course of several breath-holds. This is difficult for unhealthy patients, and impossible for pediatric patients who are typically under anesthesia throughout the exam.
I developed a model-based neural network architecture, which is trained to reconstruct vastly undersampled cardiac cine data allowing for fast cine imaging in a single breath-hold. The network is based on separable 3D convolutions to efficiently learn spatiotemporal features and relax training convergence.
This project was advised by Drs. Hui Xue and Peter Kellman from the NHLBI.
4 March 2019: Website is up!