Cardinal Robotics

The student–led team has developed a low–cost, autonomous UV–C light generating robot technology to improve surface and air sanitization while protecting the user from exposure to radiation.


Developing a tool to allow patient care teams to monitor and control blood pressure (BP) via bluetooth measurements.


Developing a unique system for delivery of Phage therapies, to address a variety of antibiotic-resistant infections.


Developing a unique therapeutic for treatment of pulmonary hypertension and other diseases characterized by inflammation and fibrosis.


Building a cell engineering platform for epigenetic reprogramming of cells via gene manipulation.


Developing a novel organ transplantation device, with the goal of increasing transplant longevity by maintaining organs at an optimal temperature throughout transplantation.

Developing an AI-based digital pathology software system to assist pathologists with more accurate pathology workflows and diagnoses.


Developing a tele-nursing application to coordinate rapid, seamless communication between patients and nurses.

Over the shoulder shot of a patient talking to a doctor using of a digital tablet

Quantitative Digitography (QDG)

Developing a remote patient monitoring system for Parkinson’s Disease to enable accurate symptom assessment and improve therapeutic care.

QuantMD (QMD)

Developing a tool to quantify surgical performance to improve surgeon training and patient outcomes.

Self–replicating Environmentally–Embedded Diagnostic (SEED)

Developing a cell–based diagnostic tool for a variety of conditions, including COVID-19, using genetically modified live bacteria


Developing an instrument-free, saliva-based molecular diagnostics solution for conditions such as COVID–19, seasonal flu, and malaria.

Stanford Pharmacogenomics Implementation and Reporting Architecture (SPIRA)

Developing a point-of-care clinical pharmacogenomics solution for patients and providers.

Transforming Aggressive Cancer Therapies (TACT)

Developing an innovative, first-in-class treatment for aggressive cancers.


Developing a machine learning approach to evaluate and improve the quality of patient–captured images.


Developing a patient intake and triage system using physician video assessment and chatbot/AI technology.