APRIL 3, 2026
Shirley B. Wang, PhD
Assistant Professor
Department of Psychology
Yale University
About the Presentation: Mental disorders are incredibly complex, heterogeneous, and dynamic phenomena. In this talk, I will present a series of studies that develop and harness novel methods to capture and model this complexity, with a focus on suicide and other forms of self-harm. First, I will describe our data-driven work using machine learning and digital phenotyping to predict who is at risk for suicide and self-harm and when risk is highest. Second, I will discuss our theory-driven work investigating why and how suicide and self-harm arise by building formal mathematical models of these phenomena as complex dynamical systems. Finally, I will highlight future directions for mathematical, computational, and digital methods to advance the understanding, prediction, and prevention of suicide and self-harm.
About the Presenter: Shirley Wang is an Assistant Professor in the Department of Psychology at Yale University, where she directs the Computational Clinical Science Lab. Shirley’s research aims to develop and harness methods that can capture and model the immense complexity of psychopathology, with a focus on suicide, nonsuicidal self-injury, and eating disorders. Her work integrates methods from across the clinical and computational sciences, including machine learning, mathematical modeling, digital phenotyping (e.g., smartphones and wearable biosensors). Her work has been funded by the National Institutes of Health, the National Science Foundation, several private foundations, and published in over 60 scientific papers and book chapters. Shirley’s research and mentorship has also been recognized through the receipt of several awards, and she was recently listed as one of Forbes’ 30 Under 30 in Healthcare.