I am a statistician, data scientist, and AI researcher with interests spanning statistical methodology, machine learning, and artificial intelligence. In 2026, I obtained my PhD in Statistics from the University of Michigan, where I was privileged to be advised by Professor Kerby Shedden. My doctoral research focused on developing flexible, non-parametric methods for studying the full conditional distribution of outcomes, especially in complex, high-dimensional, and incomplete data settings.

My research focuses on developing robust, interpretable statistical and machine learning methods—and accessible software that brings them into practice—to address real-world challenges across health, biomedical, public health, and socioeconomic domains. I have also collaborated with the AEDI on projects related to asset development, education, and financial inclusion. This work seeks to create opportunities for low-income children and families in the United States and around the world, helping to break cycles of poverty and promote upward economic mobility.

More recently, my interests have expanded to include causal inference, large language models (LLMs), and the broader intersection of statistics and artificial intelligence. I am always open to scientific collaborations, particularly in biomedicine, public health, and other interdisciplinary domains where statistical and machine learning methods can help generate meaningful insights. If you are interested in collaborating or discussing potential research opportunities, please feel free to reach out (bagyare at umich dot edu) or via the contact form.

Outside of work, I enjoy listening to music, spending time with friends and family, and following soccer as a lifelong Manchester United supporter.

Go Blue!