Elyas Sabeti

Data Science Fellow Alum

Michigan Institute for Data Science

My primary focus is on developing foundational theory and methodology for data science using information theory, machine learning and signal processing. More specifically, my data science research aims to develop theory and algorithms for analysis of medical Big Data using Data In Motion with applications in Digital Health. In particular, my planned research objective is to design robust and validated clinical, physiologic, cellular, and genomic predictive of infection, as defined by viral shedding. In particular, we will identify parameters that predict a person’s contagiousness at the earliest possible time following exposure using genomic data and real-time physiological signals recorded by wearables (Empatica, Fitbit, UnitedHealth). As such, we design novel approaches tailored to complex data (high-dimensional, missing values, time-series, multi-modality) and identify low dimensional biological signatures characterizing the host response of individuals to virus inoculation. At the completion of this study we will have developed a model for contagion that will have significant military and public health impact, since soldiers as well as the general public pose infectious risks to those around them. The data generated will have collateral benefit for the scientific community investigating host-pathogen interactions and for the diagnostics and pharmaceutical communities for development of platforms to diagnose and treat pre-symptomatic infectious disease.