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I fell in love with math when I realized how useful it is,” says , assistant professor at �����о��� Grossman School of Medicine and �����о���’s . “The magic happens when I can use math to explain how the world works.” Since 2023, Dr. Shenhav, a mathematician and computer scientist, has led a team of data scientists and computational biologists at �����о��� Langone Health’s , where she develops mathematical models and artificial intelligence–driven algorithms to advance maternal and child health. As part of a collaborative effort, Dr. Shenhav and her team partner with clinicians and biologists across multiple disciplines. Here, she shares insights into her innovative work:

I’m deeply passionate about women’s and children’s health and the application of rigorous, data-driven approaches to its study. My research group focuses on developing technology to improve health outcomes for women and children, particularly during the pivotal life stages of fertility, pregnancy, and lactation.

Our bespoke computational methods are designed to uncover hidden dynamics that distinguish normal from pathological conditions. The lab’s approach is novel. We’re working to uncover hidden, biologically meaningful patterns from large datasets. There’s no mouse model that can do this. You need a deep understanding of pregnancy and early-life biology, as well as the intricacies of mathematics and statistics, to design precise, translatable algorithms.

Our research centers on three interconnected areas: the human microbiome, human breast milk, and the dynamics of pregnancy. Unlike our DNA, our microbiome, the collection of microbes living in and on our body, is dynamic and subject to frequent changes. Our research explores how the microbiome evolves over time and how these changes can cause or indicate health and disease. To this end, we develop computational methods that serve the broader microbiome community, and apply them to elucidate the microbiome’s role in the key life stages of fertility, pregnancy, and lactation.

Working on the human microbiome made me realize that we must also look at one of its critical drivers, human milk, which is optimal for growth and development. Its composition varies between mothers and changes throughout lactation. Remarkably, we know more about what’s in a strawberry than what’s in human milk. To address this knowledge gap, we study the ecology of human milk across diverse populations worldwide. By designing computational methods and mathematical models to study how complex biological systems work—an approach known as systems biology—we aim to understand how the components and dynamics of human milk drive early-life development and shape long-term health outcomes.

In a recent study published in the journal Cell, we found that much as a pacemaker regulates the rhythm of the heart, breastfeeding and human milk set the pace and sequence for microbial colonization in an infant’s gut and nasal cavity, ensuring that this process unfolds in an orderly and timely manner. Our findings show that healthy microbiome development requires not only the presence of beneficial microbes, but also their arrival in the right order and at the right time, a process regulated by breastfeeding and human milk. This gradual maturation process, in turn, lowers a child’s risk of developing asthma.

Another major focus of our lab is understanding the dynamics of pregnancy, specifically what distinguishes a healthy pregnancy from one that results in adverse outcomes or disease. Preeclampsia, a pregnancy complication characterized by high blood pressure and potential organ damage, is one such adverse outcome, along with preterm birth, fetal growth restriction, and stillbirth. While these complications pose immediate risks during pregnancy, they are also linked to a higher lifelong risk of cardiovascular disease, stroke, and vascular dementia for women. This connection underscores how pregnancy complications may either cause or reflect underlying vulnerabilities rather than being solely isolated events. Pregnancy therefore provides a unique window into a woman’s future health, offering valuable insights to enhance maternal and fetal/infant wellbeing in the short and long term.

Hypertensive disorders of pregnancy are the second leading cause of maternal deaths worldwide. Driven by abnormal placental development and function, they profoundly affect the course of pregnancy, with lasting impacts on maternal health. However, the placenta is currently difficult to assess, and no single diagnostic test reliably detects hypertensive disorders of pregnancy early in their development.

Our lab addresses this gap by using the eye as a proxy for placental and pregnancy health. Through high-resolution, noninvasive retinal imaging and tailored algorithms, we aim to predict hypertensive disorders and other adverse pregnancy outcomes.

Research shows that vascular reactivity increases in women at risk for these disorders, with significant placental changes occurring as early as the first trimester. While previous studies have noted retinal vascular changes postpartum, we are among the first to rigorously assess these changes early in pregnancy, before clinical symptoms appear. Importantly, early identification offers a chance to prevent or treat poor outcomes for both mothers and infants. This is a collaboration with Dr. Srilaxmi Bearelly, a retina specialist at Columbia University Irving Medical Center and Columbia’s Department of Obstetrics and Gynecology.

In short, we collect data and develop algorithms to drive discoveries in areas of health that have long been underserved. It’s truly amazing to think that I’ve been able to shape my career around this work, combining my love for math and algorithms with my commitment to advancing women’s health in ways I never could have imagined.