Christopher Sandino

Ph.D. Student · Stanford University ·

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).

Research Projects

Deep learning-based reconstruction of spatiotemporal MRI data

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.

Fast, motion-robust, comprehensive cardiovascular MRI exam

Precision & accuracy of quantitative tissue characterization

This project was advised by Drs. Hui Xue and Peter Kellman from the NHLBI.


Stanford University

Doctor of Philosophy
Electrical Engineering
September 2015 - Present

Stanford University

Master of Science
Electrical Engineering
September 2015 - June 2017

University of Southern California

Bachelor of Science
Electrical Engineering
Magna Cum Laude
August 2011 - May 2015


4 March 2019: Website is up!