Broadly, my academic research approach is to find sensible and useful interpretations of large and noisy data sets through computational and statistical modeling techniques. Over my career, I have used this approach to answer very different research questions. Currently I am working on the following research projects: (1) Bayesian modeling of publicly available local area health data (funded by RWJF and UW-Madison ); (2) Predicting an individual’s health from objective and subjective measures of neighborhood quality; (3) How population vs county-based analysis of data affects the utility of Community Health Needs Assessments; (4) Leveraging big data to benefit small communities (funded by RWJF).
Before 2017, my content focus was largely on visual perception and memory and how the brain sifts through noisy sensory input to arrive at decisions about the visual world. This research trajectory was funded through an NSF CAREER award.
As a associate professor in Psychology, I have taught a variety of graduate and undergraduate courses, including Data-Motivated Storytelling, Perception, Method and Theory in Psychology, Research Methods, Experimental Psychology, Teaching in Psychology, Evolutionary Psychology, and special topics courses titled How We Decide, and Color: Psychology, Philosophy, Physics and Art. Whatever the content, my approach to teaching is the same. Because research suggests we learn best through practice, my classrooms are largely flipped: students learn initially outside of class by reading, listening to, or watching assigned material, and in-class time is spent solving problems, answering questions, working on experiments, or engaging in vigorous debate.
Major Research Interests:
- Social connections and health
- Risk and resilience in response to COVID-19
- Bayesian modeling
- Community-engaged research
- Social determinants of health
- Philosophy of perception
- Visual perception and memory
- Evolutionary psychology