Chris Glynn is a data scientist and advisor to industry and government. His research – recently featured in the New York Times, Bloomberg and World Economic Forum – seeks to improve decision-making in dynamic and uncertain environments. He builds Bayesian statistical models and computational algorithms to investigate complex dynamical systems, including the effects of local housing cost increases on homelessness and the brain's mechanism for encoding simultaneous stimuli. He is also a Zillow Research Fellow, collaborating with Zillow on projects at the intersection of data science, housing markets and public policy. Prior to joining the faculty at UNH, Glynn was a postdoctoral fellow in the Department of Statistics at the University of Washington. He earned his Ph.D. in Statistical Science from Duke University.
Ph.D., Statistics, Duke University
M.S., Statistics, Duke University
ADMN 420: Business Statistics
DS 768: Forecasting Analytics
Caruso, V. C., Mohl, J. T., Glynn, C., Lee, J., Willett, S. M., Zaman, A., . . . Groh, J. M. (2018). Single neurons may encode simultaneous stimuli by switching between activity patterns. Nature Communications, 9(1). doi:10.1038/s41467-018-05121-8
Boyer, D. M., Winchester, J. M., Glynn, C., & Puente, J. (2015). Detailed Anatomical Orientations for Certain Types of Morphometric Measurements Can Be Determined Automatically With Geometric Algorithms. The Anatomical Record, 298(11), 1816-1823. doi:10.1002/ar.23202
Boyer, D. M., Puente, J., Gladman, J. T., Glynn, C., Mukherjee, S., Yapuncich, G. S., & Daubechies, I. (2015). A New Fully Automated Approach for Aligning and Comparing Shapes. The Anatomical Record, 298(1), 249-276. doi:10.1002/ar.23084