About
Neil Spencer is an Assistant Professor in the Department of Statistics at the University of Connecticut. His primary research focuses on robust Bayesian inference, statistical network analysis, hierarchical Bayesian modeling, and efficient Bayesian computation, with applications in forensic footwear analysis and network data. He teaches statistical computing courses including STAT5410 for the Master of Data Science program.
His educational background includes a joint PhD in Statistics and Machine Learning from Carnegie Mellon University, an MSc in Statistics from the University of British Columbia, and a BScH in Mathematics and Statistics from Acadia University. Previously, he was a postdoctoral researcher in Biostatistics at Harvard School of Public Health.
Dr. Spencer's recent publications demonstrate a strong focus on Bayesian methodologies applied to network modeling, computational statistics, and experimental design. His work frequently appears in top-tier statistics journals such as the Annals of Statistics.
