.jpg "Liat Shenhav, PhD photo")
Assistant Professor
New York University
Liat Shenhav is an Assistant Professor at the Institute for Systems Genetics and departments of Microbiology and Computer Science at NYU. Before joining NYU, Liat served as an independent research fellow at the Center for Studies in Physics and Biology at the Rockefeller University. She holds a B.Sc. and M.Sc. in Mathematics and Statistics from Tel-Aviv University and earned her Ph.D. in Computer Science from the University of California, Los Angeles.
Liat is a computer scientist with a strong background in computational biology and over a decade of experience in data science across industry and academia. Liat specialize in designing bespoke machine learning models. These models integrate representations of temporal trajectories and statistical learning, uncovering latent dynamics distinguishing between normal and pathological conditions across biological systems. Liat's approach facilitates the discovery of biologically interpretable relationships in the temporal dynamics of multiple omic layers, particularly in connection with health or development-related phenotypes.
Liat's objective is to leverage the latest advancements in data collection, high-throughput imaging and sequencing with cutting-edge artificial intelligence algorithms. By doing so, Liat aims to advance the study of pregnancy and lactation, enabling early predictions of pregnancy, birth, and lactation-related phenotypes that impact both mothers and infants.