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Invited Speakers




Rebecca Doerge

Rebecca Doerge

Carnegie Mellon University


Title: The Carnegie Mellon University Cloud Lab: Automating science for the future

Biography: Rebecca Doerge is the Glen de Vries Dean of the Mellon College of Science at Carnegie Mellon University. Prior to joining both the Department of Statistics and Data Science and the Department of Biology at Carnegie Mellon University Rebecca was the Trent and Judith Anderson Distinguished Professor of Statistics at Purdue University. Dean Doerge joined Purdue University in 1995 and held a joint appointment between the Colleges of Agriculture (Department of Agronomy) and Science (Department of Statistics) until her departure. Dean Doerge's research program focuses on Statistical Bioinformatics, a component of bioinformatics that brings together many scientific disciplines to ask, answer, and disseminate biologically interesting information in the quest to understand the ultimate function of DNA and epigenomic associations. Rebecca is the recipient the Teaching for Tomorrow Award, Purdue University, 1996; University Scholar Award, Purdue University, 2001-06; and the Provost's Award for Outstanding Graduate Faculty Mentor, Purdue University, 2010. She is an elected Fellow of the American Statistical Association (2007), an elected Fellow of the American Association for the Advancement of Science (2007), and a Fellow of the Committee on Institutional Cooperation (CIC; 2009). She is Chair of the American Association for the Advancement of Science (AAAS) Section U (Statistics). Dean Doerge has published over 140 scientific articles, published two books, and graduated 26 PhD students.


Lucia Gutierrez

Lucia Gutierrez

University of Wisconsin-Madison

Title: Designing experiments for large multi-environment genomic studies: Mega-environmental designs (MED) optimization for sparse testing

Biography: Dr. Gutierrez is an Associate Professor and the Cereals Breeder and Quantitative Geneticist at the University of Wisconsin-Madison. Dr. Gutierrez earned her Ph.D. in Plant Breeding from Iowa State University and spent time as a postdoc at the Biometris Unit at Wageningen University and Research Center in the Netherlands. She was an Associate Professor in Statistical Genetics at the Universidad de la Republica in Uruguay before joining UW-Madison. Dr. Gutierrez has a strong international recognition being regularly invited to present her research in quantitative genetics and to teach advanced courses for graduate students, researchers, and plant breeders abroad. Dr. Gutierrez has served professional societies and the academic community in several roles including being Chair of the Plant Breeding and Genetics Division of the Crop Science Society of America, member of the Oat Global Board of Directors, the International Oat Nursery Executive and Genomics Committees, and the Steering Committee of the Agricultural Genomes to Phenomes Initiative, a multimillion USDA initiative to support research connecting high throughput phenotyping with genomic data. Her research focuses on resource optimization for large genomic studies and the understanding of the genetic architecture of complex traits and their response to the environment. Her research includes experimental design optimization for large genomic studies using spatial modeling and geographical information systems, genomic data, high throughput phenotypic information, classical experimental design theory, and genotype by environment interaction modeling to better predict the genotypic performance of individuals. She integrates state of the art genotyping technologies with large phenotyping experiments to study complex traits and the mechanisms employed by plants for local adaptation including the study of biotic and abiotic interactions. Her research program has also an applied component, which combines strong theoretical development, genomic tools, and high throughput phenotyping to release cereals cultivars to serve the U.S. agricultural systems. Dr. Gutierrez has mentored more than 25 graduate students, 25 undergraduate students and served in more than 45 graduate student committees in countries as diverse as the U.S., Uruguay, Argentina, Brazil, Sweden, Australia, and Canada. Her research career includes the publication of more than 50 papers in high impact journals, securing several million dollars in research funds for her research, and the release of 5 oat cultivars.




Mevin Hooten

University of Texas at Austin


Title: Geostatistical capture-recapture models

Biography: 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 and received the Distinguished Achievement Award from the ASA Section on Statistics and the Environment in 2022.  He serves as Associate Editor for Biometrics, Environmetrics, and The Journal of Agricultural, Biological, and Environmental Statistics. He has authored 3 books and more than 170 scientific publications in the areas of Bayesian and spatio-temporal statistics with applications in ecology, epidemiology, and environmental science.


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Marcia Almeida Macedo


Title: Data-driven perspectives in a commercial breeding pipeline

Biography: Marcia Almeida de Macedo, Ph.D., is a senior statistician within the Analytics and Data Sciences Group at Syngenta Seeds. She holds a doctorate degree in Statistics and Ecology, Evolution, and Organismal Biology from Iowa State University.  Her background is in engineering, and she has earned bachelor’s and master’s degrees in electrical and biomedical engineering. At Syngenta, Dr. Almeida de Macedo guides the complex design decisions and analyses of large-scale plant breeding field experiments using advanced statistical modeling methods to predict plant phenotype (performance) based on genotype, the environment, and their interactions.  Related to her research findings, Dr. Almeida de Macedo has contributed several peer-reviewed publications applying statistics in an array of fields including public health, medicine, and bioinformatics. Notably, she is the first author of the highly accessed 2013 BMC Bioinformatics publication Comprehensive analysis of correlation coefficients estimated from pooling heterogeneous microarray experiments ( Dr. Almeida de Macedo is serving the American Society of Agronomy as the 2023 chair of the Biometry and Statistical Computing section. She was also the 2020 leader of the Statistical Education/Training for Researchers of the American Society of Agronomy.



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Larry Madden

The Ohio State University

Title: Models and methods for network meta-analysis in the plant and agricultural sciences

Biography: Laurence Madden received his Ph.D. from The Pennsylvania State University in plant pathology, with an area of specialization in statistics. He was a faculty member in the Department of Plant Pathology at The Ohio State University for over 42 years, being appointed to the endowed position of Distinguished Professor of Plant Protection in 2008. Madden is an authority in plant disease epidemiology, specializing in the modeling of plant disease dynamics in time and space. Much of his research has involved the application of linear, generalized linear, and nonlinear mixed models in characterizing epidemics in crops and quantifying disease controls. He has taught many workshops on mixed models in plant pathology. He and colleagues have developed several prediction systems that are used to identify high-risk periods for disease outbreaks and recommend control interventions. Prompted by one original application, meta-analysis has now dominated much of his work for the past 15 years. Madden has published over 275 journal articles and has written two textbooks. He is a former president of the American Phytopathological Society (APS). He has received several national and international awards and is an elected fellow of two societies. He retired in September 2022 but is continuing several research projects. 


Robert Tempelman

Robert Tempelman

Michigan State University

Title: Addressing reproducibility in cross-validation schemes for heterogeneous field data. 

Biography: Rob Tempelman is a Professor of Animal Science and Director of the Statistical Consulting Center for the College of Agriculture and Natural Resources at Michigan State University. He received his Bachelor and Master of Science degrees at the University of Guelph, Canada and his PhD in Dairy Science from the University of Wisconsin-Madison. Dr. Tempelman’s research program has primarily focused on hierarchical Bayesian applications to problems in animal science and quantitative genetics with more recent emphases placed on multiple trait modeling issues as they pertain to inferences on composite traits such as feed efficiency in dairy cattle. He is the author of over 135 peer-reviewed publications and was the 2017 recipient of the J.L. Lush Award in Animal Breeding from the American Dairy Science Association.


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