CS Colloquium: Narges Razavian (NYU)

Machine Learning in Medicine: Disease Prediction and Biomarker Discovery

Machine learning has seen great progress in the past decade. In parallel, Electronic Health Records (EHR) systems are accumulating clinical and medical data at unprecedented scales. The intersection of the two phenomena has enabled multitude of machine-learning-assisted medical care, with potential to impact and improve healthcare research, and delivery for millions of individuals. In this talk, we first briefly review the data landscape of healthcare, including modalities and quantities of data available to various machine learning tasks, and discuss the implications of this data on different research areas. We will then focus on a number of recent work at my research lab at NYU Langone Medical Center on the topics of biomarker discovery and disease classification. Our discussion includes classification of lung cancer genomic mutation and subtype using histopathology images; deep learning on clinical notes for disease prediction; and biomarker discovery using EHR time series.

Bio: Narges Razavian is an assistant professor at NYU Langone Medical Center, with joint appointment at departments of Radiology and Population Health. Before that, she was a postdoc at NYU Courant CILVR lab, as a member of David Sontag's team. Her lab is currently focusing on machine learning and deep learning applications in healthcare, and is actively working on medical notes, EHR time series, and medical imaging.

Friday, April 5, 2019 at 11:00am

St. Mary's Hall 414

Event Type

Academic Events


Georgetown College, Computer Science



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