Google Scholar : PUBLICATION
Spatial or Spatio-temporal data are continuously gaining increasing interest, and it is now commonplace to consider geographical aspects of health outcomes with applications to public health and/or preventive medicine. For example, the world is experiencing the COVID-19 pandemic, a great threat to our public health, and we can study its mortality rate at the early stage considering the spatio-temporal correlation of the infectious disease over the regions1. I have been broadly studying theories, methodologies, and interdisciplinary applications that can be used to analyze such data within the disciplines of Statistics, Epidemiology, and Statistical Learning. Over the past 5 years, I have participated in various collaborating research such as air pollution epidemiology, remote sensing, microbiome data analysis, and tobacco health effect estimation.
Highlights
- SEPTEMBER, 2022 : Paper presentation (2022), An Efficient Active Learning Design through Random Forest under Covariate, Fall conference, Center for Survey Statistics & Methodology, Iowa State University
- AUGUST, 2022 : Poster presentation (2022), Nonparametric estimation of the autocovariance of a Gaussian Process model in time series, Expressing and Exploiting Structure in Modeling, Theory, and Computation with Gaussian Processes, The Institute for Mathematical and Statistical Innovation (IMSI) workshop
- APRIL, 2022 : Open the Github Resaerch webpages
- JANUARY, 2022 : Paper presentation (2022), Random Forest Prediction Intervals for Spatially dependent data, Spring conference, Center for Survey Statistics & Methodology, Iowa State University