Associate Professor of Urban Analytics;
Associate Professor of Computer Science and Public Service;
Director, Machine Learning for Good Laboratory
Daniel B. Neill, Ph.D., is Associate Professor of Computer Science, Public Service, and Urban Analytics at CUSP, with tenured appointments at NYU’s Courant Institute Department of Computer Science and Robert F. Wagner Graduate School of Public Service, and affiliated appointments at NYU’s Department of Computer Science and Engineering (NYU Tandon) and Center for Data Science. He directs the Machine Learning for Good Laboratory and recently finished a term as co-director of the NYU Urban Initiative. His research focuses on developing novel machine learning methods for social good, with applications ranging from medicine and public health to fairness in criminal justice.
Dr. Neill has been a faculty member at CUSP since 2016. His Machine Learning for Good Lab develops and applies novel methodological approaches to address critical urban problems. He works closely with organizations including health departments, hospitals, and city leaders to create and deploy data-driven tools and systems to improve the quality of public health, safety, and security, for example, through early detection of disease outbreaks and targeted interventions to combat the opioid crisis. He leads an NSF-funded project on Fairness in Artificial Intelligence, with the goal of maximizing benefits and minimizing harms of AI for public-sector decision making.