Samuel Revolinski is a Ph.D. student at Washington State University under the direction of Dr. Zhiwu Zhang. Samuel received his bachelors’ degree in Plant Science from the University of Minnesota where he developed skills in quantitative plant breeding and statistical modeling. The focus of Samuel’s research is using cutting edge predictive models and machine learning tools to improve the impact of remote sensing phenotyping on wheat breeding. He has been developing methods to detect the location of particular varieties using time series of multi spectral images so that phenotypes from fields in production can be used in genetic studies.
Using the same time series Samuel is interested in predicting agronomic traits and function-valued traits to improve the capabilities of plant breeders. Additionally, using (ANN) artificial neural networks for creating auto-encoders Samuel has developed a methodology and python module for visualization and modeling of population structure for genome wide association studies (GWAS). The methodology he developed is showing promising results for its use to control population structure in GWAS. Samuel aspires to be a tech-savvy plant breeder who uses his statistical and computational skills to develop cultivars for a changing climate by using drone and satellite platforms to perform remote sensing phenotyping.