GEAT Seminar Fall 2017: Dr. Chris Wikle September 26


The GEAT department welcomes Dr. Chris Wikle, University of Missouri, as our next seminar speaker.

Title: The Interface Of Deep Learning and Spatio-Temporal Statistics in the Geosciences

When: Tuesday, September 26 @ 4:10 pm
Where: 2050 Agronomy Hall

Bio: Christopher K. Wikle is Curators’ Distinguished Professor of Statistics at the University of Missouri, with additional appointments in Soil, Environmental and Atmospheric Sciences and the Truman School of Public Affairs. He received a PhD co-major in Statistics and Atmospheric Science in 1996 from Iowa State University. He was research fellow at the National Center for Atmospheric Research from 1996-1998, after which he joined the MU Department of Statistics. His research interests are in spatio-temporal statistics applied to environmental, geophysical, agricultural and federal survey applications, with particular interest in dynamics. Awards include elected Fellow of the American Statistical Association (ASA), Distinguished Alumni Award from the College of Liberal Arts and Sciences at Iowa State University, ASA ENVR Section Distinguished Achievement Award, co-awardee ASA Statistical Partnership Among Academe, Industry, and Government (SPAIG) Award, the MU Chancellor’s Award for Outstanding Research and Creative Activity in the Physical and Mathematical Sciences, the Outstanding Graduate Faculty Award, and Outstanding Undergraduate Research Mentor Award. His book Statistics for Spatio-Temporal Data (co-authored with Noel Cressie) was the 2011 PROSE Award winner for excellence in the Mathematics Category by the Association of American Publishers and the 2013 DeGroot Prize winner from the International Society for Bayesian Analysis. He is Associate Editor for several journals and is one of six inaugural members of the Statistics Board of Reviewing Editors for Science.

Abstract: Spatio-temporal data are ubiquitous in the geophysical, agricultural, ecological and environmental sciences, and their study is important for understanding and predicting a wide variety of processes. One of the chief difficulties in modeling spatial processes that change with time is the complexity of the dependence structures that must describe how such a process varies, and the presence of high-dimensional datasets and prediction domains. It is particularly challenging to specify parameterizations for nonlinear dynamical spatio-temporal models that are simultaneously useful scientifically and efficient computationally. Statisticians have developed some “deep” mechanistically-motivated models that are useful and do a good job of accommodating the uncertainties in the predictions and inference. However, these models can be expensive to run. On the other hand, the machine learning community has been developing more black-box “deep learning” approaches, which can be quite flexible, and in a few cases, can be implemented quite efficiently, but without formal uncertainty quantification. Here, we discuss models at the interface of statistics and machine learning, and present some hybrid methods – in one case where traditional analog models from atmospheric science can be used for general spatio-temporal prediction, and one case where a class of recurrent neural networks can be used to perform long-lead prediction. In both cases, we discuss the importance of uncertainty quantification. The methods will be illustrated with long-lead prediction of soil moisture over Iowa, settling patterns of migratory waterfowl, and ENSO.