About me
I am an Assistant Professor in the Department of Geography, University of Oregon. My research program focuses on advancing Bayesian probabilistic inferences in spatial data analysis, promoting the application of spatial and spatiotemporal analysis in public health, informing the development of geographically tailored, evidence-based health intervention programs, and ultimately improving population health.
My work uses both Markov Chain Monte Carlo (MCMC) and Integrated Nested Laplace Approximation (INLA) algorithms to implement Bayesian spatial and spatiotemporal models. These models explicitly address common challenges in statistical analysis of geospatial datasets at small-area levels (e.g.,census tracts and zip codes), including but not limited to spatial/temporal autocorrelation, space-time interaction, zero-inflation, data suppression (censoring), and multivariate modeling. I also apply machine learning to analyze geospatial mobile health data collected from devices including AppleWatch and Fitbit.
Education
- Ph.D in Planning, University of Waterloo, 2017
- M.S. in Cartography and Geographic Information System, Wuhan University, 2011
- B.S. in Geographic Information System, Wuhan University, 2009
Prospective graduate students
I always welcome talented graduate students to work with me on spatial and spatiotemporal statistics, Bayesian modeling, machine learning, and their applications in various geographical phenomena. Check out this position for Fall 2024! Link
Recent news
I am joining the editorial board of Spatial and Spatio-temporal Epidemiology!
I am hornored to be selected as a Vu fellow, 2021-22! Click for more details
CFP AAG 2022: “Spatiotemporal disease mapping and analysis”. Click for more details