2022 Dates

May 16-19, 2022
Utah State University
Logan, Utah

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Invited Speakers

Keynote and Workshop Speaker

Guilherme J. M. Rosa

Guilherme J. M. Rosa

Regression and classification applied to precision agriculture

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. Guilherme Rosa started his professional career 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 at the University of Wisconsin-Madison (since 2006), with an affiliate appointment at the Department of Biostatistics & Medical Informatics.

Guilherme Rosa teaches courses and develops research on statistical and computational tools for the analysis of livestock data, including beef and dairy cattle, swine, and poultry among others. Examples of applications include the analysis of farm-level operational data for optimization of management practices, high-throughput phenotyping techniques for real-time monitoring of individual animals and disease surveillance, as well as quantitative genetics/genomics and breeding. Guilherme has published 10 book chapters and over 200 refereed papers in scientific journals and has funded his program with outside grants valued at over $10 million.

Invited Session Speakers

Philip M. Dixon

Philip M. Dixon

Model averaging in agriculture and natural resources: what it is, when it is useful, and when it is a distraction

Philip Dixon is a Professor in the Department of Statistics at Iowa State University. His favorite research is developing and evaluating statistical methods to answer interesting biological questions. He prefers working with ecologists, wildlife biologists, agronomists, and animal scientists, where he can see what is being measured. A lot of this work is collaborative. The themes are using likelihood inference in non-standard situations and using computer-intensive methods. Some of the current projects are understanding foundational issues in the analysis of agronomic data, especially from repeated experiments, using telemetry to estimate butterfly locations, modeling physical activity data, and developing model-based visualizations of species composition data.

Mevin B. Hooten

Mevin B. Hooten

Recursive computing strategies inspire new model specifications

Mevin Hooten is a Professor in the Department of Statistics and Data Sciences at The University of Texas at Austin. He was elected Fellow of the American Statistical Association in 2017, is the 2021 Chair of the ASA Section on Statistics and the Environment, and serves as Associate Editor for Biometrics, The Annals of Applied Statistics, Environmetrics, and The Journal of Agricultural, Biological, and Environmental Statistics. He has authored 3 books and more than 160 scientific publications in the areas of Bayesian and spatio-temporal statistics with applications in ecology, epidemiology, and environmental science.

Normand St-Pierre

Normand St-Pierre

Advances in statistics: 2 steps forwards, 3 steps backwards…

Normand St-Pierre is Professor Emeritus of Animal Sciences at The Ohio State University and Director of Research and Technical Services for Perdue AgriBusiness. He grew up in Québec, Canada, where he received his B.S. in Animal Science and M.S. in Animal Nutrition, followed by a Ph.D. in Dairy Science with minors in statistics and economics in 1985 from The Ohio State University. After ten years in the private sector, he joined the Department of Animal Sciences at Ohio State, where he conducted research and extension programs in the area of farm management, nutrition, and biometrics until his retirement from the university in 2016. Dr. St-Pierre has published over 700 articles in various journals and has received numerous awards for his research and extension work. When not around cows or cow people, you will find him riding or fixing one of his 12 bicycles or daydreaming on his beloved sailboat. 

Juan P. Steibel

Juan P. Steibel

What are animal scientists learning from using deep learning?

Juan P. Steibel is an Associate Professor of Animal Science and Fisheries and Wildlife at Michigan State University. His academic background includes a B.S. in agronomy from National University of La Pampa – Argentina in 1996, an M.S. in Biometry from University of Buenos Aires – Argentina in 2002, and a PhD in Animal Science from Michigan State University in 2007. Juan P. Steibel investigates the development, adaptation, and application of statistical and computational methods to advance swine production and breeding through the integration of multi-omics data streams. For instance, his research spans expression QTL mapping in structured populations, meta-analysis of genome-wide association from multiple genomic evaluations, high throughput behavioral phenotyping in group-housed pigs, and the incorporation of behavioral measures into the estimation of social effects.