May 16-19, 2022
Utah State University
Optional Workshops: Regression and Classification Applied to Precision Agriculture
Vast amounts of data are routinely collected in agriculture, including traditional operational farm data and, more recently, data from digital tools such as remote and on-site sensing technologies. Moreover, many additional sources of information can be combined with farm data, such as economic and weather variables. The integration and analysis of such data can generate important insights and data-driven decision tools for optimization of agriculture systems. Nonetheless, suitable statistical and data mining tools are required to cope with such large and complex observational datasets, which involve multicollinearities and redundancies, nonlinear relationships, and spatial and temporal dependencies. In this workshop, we will discuss regression and classification tolls for both prediction and causal inference with numerical and categorical outcomes, with special interest on agricultural data applications. Specific modelling approaches will include generalized additive models, structural equation models, and mixed-effect models. Useful algorithms and fitting strategies to be discussed will comprise dimension-reduction techniques, regularization approaches such as penalized regression and Bayesian hierarchical methods, and cross-validation strategies for variable selection and model comparison. The methods and applications will be illustrated with examples using infrared spectroscopy data, satellite remote sensing, image analysis and computer vision, among others.
Dr. Guilherme J. M. Rosa
Department of Animal and Dairy Sciences
Department of Biostatistics & Medical Informatics
University of Wisconsin-Madison
Guilherme Rosa obtained an M.S. in Animal Sciences from Sao Paulo State University (UNESP) – Brazil in 1994 and a Ph.D. in Statistics and Agricultural Experimentation from the University of Sao Paulo (USP) – Brazil in 1998. He started his professional carrier as a faculty member of the Department of Biostatistics at UNESP (1994-2001) before moving to the USA as a faculty member at Michigan State University (2002-2006). He is currently a Professor at the Department of Animal and Dairy Sciences and the Department of Biostatistics & Medical Informatics at the University of Wisconsin-Madison (since 2006).
Guilherme teaches courses and develops research on quantitative genetics and statistical genomics, including design of experiments and data analysis tools. Some specific areas of interest include mixed effects models, Bayesian analysis, and Monte Carlo methods. More recently, Guilherme has been working also on the analysis of observational data in agriculture and precision livestock management tools, using a variety of statistical and machine learning methods.
Guilherme has published 12 book chapters and over 200 refereed papers in scientific journals and has funded his program with outside grants valued at over $10 million. He has been awarded with the LeClerg Rotary Lecturer from the Biometrics Program at the University of Maryland (2011), and the Rockefeller Prentice Memorial Award in Animal Breeding and Genetics by the American Society of Animal Science (2016). He has also received the Pond Research Award (2013), the Vilas Faculty Mid-Career Investigator Award (2017), the Excellence in International Activities Award (2017), and the Kellett Mid-Career Award (2021) from University of Wisconsin-Madison.