2001 N. Soto Street , Los Angeles, CA 90033

Preterm birth is the single largest cause of death in children under 5 years of age. Those neonates who survive a premature birth often suffer from various comorbidities extending well into adulthood. Recent technological advances in artificial intelligence, biological assays, and electronic health records provide novel opportunities to unravel the complex biology of pregnancy. Immunological changes during pregnancy are highly dynamic and involve multiple interconnected biological systems. I will discuss a series of studies using single cell immune profiling, microbiome, and proteome, and transcriptome analysis of normal and pathological pregnancies, and novel machine learning approaches for integration of these modalities by accounting for prior knowledge, dataset modularity, and social determinants of health. The final section of the presentation will focus on integration of electronic health records data with biological assays to enable a precision-medicine approach to maternal and neonatal morbidity.

 

Nima Aghaeepour is an Associate Professor at Stanford University. His laboratory develops machine learning and artificial intelligence methods to study clinical and biological modalities in translational settings. He is primarily interested in leveraging multiomics studies, wearable devices, and electronic health records to address global health challenges. His work is recognized by awards from numerous national and international organizations including the Bill and Melinda Gates Foundation, the Alfred E. Mann Foundation, the March of Dimes Foundation, the Burroughs Wellcome Fund, and the National Institute of General Medical Sciences.

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