Developed a prototype device to monitor changes in microcirculation for critically ill patients as an indicator of sepsis onset, recovery and deterioration. Machine learning algorithms were used to predict the likelihood of patients becoming septic. The device includes an array of optical-sensors enveloped in a device to monitor changes in blood volume near the surface of the skin, as well as monitor patient vitals over time (i.e. changes in heart rate, pulse volume, respiratory rate, blood pressure, etc.). Led a pilot study, worked with physicians within the Hospital of University of Pennsylvania’s (HUP) Medical Intensive Care Unit (MICU) to gather data on patients and evaluate the effectiveness of the prototype device.