The Event Analytics and Statistical Learning Lab develops statistical and machine learning methods to predict, detect, and link events in complex systems. Our research spans event forecasting with point processes and Hawkes models, anomaly detection in networks and spatial data, and data linkage across disparate sources. Applications range from crime, terrorism, and traffic safety to organ transplantation, disease surveillance, and social dynamics. The lab advances the statistical foundations of data science while delivering insights with direct impact on public safety, security, and health.
Research Tags: Event Forecasting; Statistical Learning; Machine Learning and Data Mining; Anomaly Detection; Crime Linkage; Transplant Analytics
Research Areas
Artificial Intelligence and Machine Learning
Biomedical, Health, and Life Sciences
Business, Markets, and Policy
Computational Sciences
Data Ethics and Society
Engineering, Robotics, and Physical Sciences
Environment and Climate Science
Theory, Foundations, and Advanced Methodologies
Faculty
