Negin earned her PhD in Computer Science from Purdue University and is broadly interested in applications of Machine Learning and Topological Data Analysis, particularly in health and human well-being.

She is currently a Postdoctoral Associate in the Getz Lab at the Broad Institute. Her current research focuses on developing predictive models for determining the genetic dependencies of cancer cell lines using Graph Neural Networks.

Negin’s previous research projects include mining sequential and textual data using Topological Data Analysis, Semi-Supervised, and Active learning.