Liat Shenhav

Liat Shenhav is an Assistant Professor at New York University, affiliated with the Department of Microbiology and the Department of Obstetrics and Gynecology. Her research focuses on computational biology, mathematical models, and artificial intelligence (AI) to improve maternal and child health. She develops algorithms that integrate tensor factorization, time-series analysis, and machine learning to study the human microbiome, human milk, and pregnancy dynamics. Her work explores how these biological systems influence health outcomes, particularly in fertility, pregnancy, and lactation. Shenhav's research applies systems biology and data science to uncover the role of the microbiome and human milk in early-life development and long-term health. Her interdisciplinary approach combines insights from microbiome research, human milk science, and clinical data to advance maternal and child health.

Liat Shenhav is an Assistant Professor at New York University, specializing in microbiome research and computational biology. Her work focuses on understanding microbial community dynamics, particularly in the gut and vaginal microbiomes, and how they are influenced by factors such as diet, age, and breastfeeding. She develops computational tools and statistical methods to analyze high-throughput data, including DNA methylation and microbiome sequencing, to uncover patterns and mechanisms underlying microbial assembly and function. Her research also explores the role of microbiota in health conditions such as keratoconus and Alzheimer’s disease, emphasizing the interplay between microbial communities and host physiology.