Advancing Research in Cardiovascular Health
Innovation at the intersection of cardiothoracic surgery and machine learning.
Complex diseases require multidisciplinary solutions.
Hospitals worldwide generate millions of medical records that are processed and interpreted by medical experts everyday. From recorded surgeries and annotated medical records, to pathology reports and genomic data, they sit on vast troves of data primed for medical research and innovation.
By bringing together approaches from engineering and clinical medicine, we aim to leverage recent advances in natural language processing, computer vision, biomechanics, and transcriptomics to translate research from the workbench to the patient's bedside.
A code-first approach to state-of-the-art research.
We aspire to make discoveries with impact. From modeling myocardial infarctions to synthesizing therapeutic proteins for failing hearts, our goal is to understand and treat cardiovascular disease. Core to our approach is sharing research and tools to fuel progress in the field. Our researchers regularly publish in academic journals and release projects as open source.
Going big for a bigger impact.
Dedicated computational servers
We have four dedicated servers on the Stanford Sherlock High Performance Computing cluster along with hundreds of CPU cores, and an array of GPUs.
Our lab curates one of the largest datasets of echocardiograms thanks to extensive collaborations and industry partners.
Our lab curates one of the largest datasets of MRIs thanks to extensive collaborations and industry partners.
Down to the molecular level
Our wet-lab resources include a single cell RNA sequencing dataset of 20,000 cardiac intersitital cells from mouse hearts exposed to a variety of treatments following a heart attack.
We Are Hiring
An open and inclusive environment.
Our team is comprised of individuals of various nationalities, ages, and socioeconomic backgrounds, all working towards a common goal of understanding and improving cardiovascular health.
We dream big and encourage learning from failure.
We thoroughly consider the consequences of our work and care deeply about real-world implications.
Most of our biggest works grow out of collaborations across different labs.
Ready to work with us? We're looking for talented people in a variety of roles across research, medicine, engineering, and biology.