Assistant Professor We study and develop in silico methods for quantifying spatiotemporal phenomena in the context of improving public health. This takes several forms: high-throughput image analysis of GFP-tagged z-stacks, machine learning to detect ciliary motion abnormalities in high-speed videomicroscope data, or applied statistics to predict and identify disease outbreaks. Research Research Interests: systems biology, bioimage analysis, data science Other Information Other Affiliations: http://quinngroup.github.io/
