Dr. Soutir Bandyopadhyay
Soutir Bandyopadhyay is an Associate Professor in the Department of Applied Mathematics and Statistics at Colorado School of Mines. He earned a doctorate in Statistics (2010) at Texas A&M University. Before joining Mines, he was an Assistant Professor in the Department of Mathematics (2010-2017). He is also a visiting scientist at the Computational and Information Systems Laboratory at the National Center for Atmospheric Research studying climate models. His core research approaches problems related to the analysis for spatial and time series data. His expertise lies in developing novel inferential procedures under spatial (and/or temporal) dependence and investigating their asymptotic properties. Although his research is primarily theoretical in nature, it has always been motivated by and applied to problems arising from practical situations in various areas such as climate science, environmental studies, finance, and biomedical studies, among others. He is an elected member of International Statistical Institute and the recipient of the International Indian Statistical Association Young Statistical Scientist Award. He serves as Associate Editors for Statistics and Probability Letters and Sankhya.
Dr. William Kleiber
Will Kleiber is an Associate Professor and Graduate Program Chair in the Department of Applied Mathematics at the University of Colorado Boulder. He received his PhD in Statistics from the University of Washington and was a postdoctoral researcher in the Institute for Mathematics Applied to Geosciences (IMAGe) at the National Center for Atmospheric Research. In 2016 he was elected the Lebesgue Chair at the University of Rennes, France, and received the Young Investigator Award from the American Statistical Association’s (ASA’s) Section on Statistics and the Environment. He has been on the editorial board of the Annals of Applied Statistics, Environmetrics, Advances in Statistical Climatology, Meteorology and Oceanography as well as Stat. He was the publications officer for The International Environmetrics Society and is currently the publications officer elect for the ASA’s Section on Statistics and the Environment. His research interests are in spatial statistics, geostatistics, statistical climatology, stochastic weather generators, uncertainty quantification for geophysical applications and statistics for energy science. He co-taught, along with D. Nychka, a STATMOS short course on Spatial Statistics in Iowa in 2019.
Dr. Douglas Nychka
Douglas Nychka is a statistical scientist with an interest in the problems posed by geophysical data and in general methods for fitting curves and surfaces to data. Currently he is professor in the Applied Mathematics and Statistics Department at Colorado School of Mines. His Ph.D. (1983) is from the University of Wisconsin and he subsequently spent 14 years as a faculty member at North Carolina State University. Following his interest in environmental data he moved to the National Center for Atmospheric Research (NCAR) 1997 to lead collaborative research between statistics and the geosciences and later directed the Institute for Mathematics Applied to Geosciences, an interdisciplinary component at NCAR. Nychka has coauthored more than 100 publications and is the primary author of two statistics packages, fields and LatticeKrig. His current interests are in quantifying the uncertainty of numerical experiments that simulate the Earth’s present and possible future climate as well as spatial statistical methods applied to large data sets. He received the Jerry Sacks Award for Multidisciplinary Research (2004), Achievement Award at the International Meeting on Statistics and Climatology (2013) and is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics.
Large and innovative spatial data are now ubiquitous across science and engineering ranging from the microscale properties of 3D printed materials to the exposure of populations to pollutants to the global views of our planet from satellites. The challenge to statistical science is to adapt methods from geostatistics to these new problems. Large data sets break traditional spatial methods and multivariate spatial data are not well modeled by classical approaches. This course will provide a hands-on and modern introduction to spatial data, followed by methods for large and nonstationary data and models for multivariate processes. It will be taught by active researchers in this area who have contributed to theory, new methods, and maintain software that makes spatial data analysis easy and accessible.
We seek to reach graduate students who are interested in challenging data problems to motivate new research topics or to enlarge their toolbox of statistical methods. This course is also a cogent overview for the faculty member who is always been curious what spatial statistics is all about. Finally, this course is for data scientists who would like to see the use of statistics for drawing inferences and quantifying uncertainty for spatial data as opposed to just forming prediction.
This course is designed as an introduction to some modern methods and applications of spatial statistics. Topics will include exploratory data analysis for spatial fields, geostatistics, spatial prediction and kriging, including modern approaches to large and multivariate data. Applications in statistical climatology and atmospheric science will be explored.
This course will use R and we recommend also using RStudio as the way to edit and run R code. Both are free and available for many different operating systems. The computing and data analysis will leverage the functions and data sets in the fields and LatticeKrig packages. These packages are specifically designed to provide different levels of options and control and make it easy to get an introduction to this subject but also to pursue more complicated analyses.