Advances in Machine Learning for Predicting and Monitoring Stress and Resilience

Speaker:

Katharina Schultebraucks, PhD
Associate Professor, Dept. of Psychiatry and Population Health
NYU Grossman School of Medicine

Abstract:

Dr. Schultebraucks applies machine learning to capture the high-dimensionality of candidate predictive models for clinical outcomes. The overall goal is to develop scalable algorithms that can be used to support clinical decision-making by complementing the clinical perspectives of clinicians with data-driven prediction models. In particular, she investigates naturalistic, longitudinal and prospective studies to identify complex sets of early predictors for stress pathologies after traumatic events. Her research develops predictive models for PTSD using data routinely collected in the Emergency Room. In addition, she is working on projects to identify risk factors in high-risk populations such as veterans of the US military or United Nations workforce from around the world. She is also using advanced machine learning methods, such as deep learning to identify transdiagnostic multi-model markers of maladaptive stress response using digital phenotyping.

Dr. Schultebraucks received a Ph.D. degree in psychiatry from the renowned Charité Medical University in Berlin, Germany in 2017. Subsequently she was awarded a prestigious research fellowship from the German Research Foundation (DFG) to pursue postdoctoral research at NYU’s Grossman School of Medicine. From 2019 through 2022, she held an appointment as Assistant Professor of Behavioral and Cognitive Sciences at the Columbia University Irving Medical Center, and was the Director of Computational Medicine and AI. In fall 2022, she re-joined NYU Grossman School of Medicine as Associate Professor of Psychiatry and Population Health.