Hafedh Ben Zaabza was born and raised in Tunisia. After completing a bachelor’s degree at Sfax, he went on to finish an engineer’s degree in agriculture science at Carthage University in 2011, a Master of Science degree in animal breeding and genetics in 2013 and a PhD in quantitative genetics at Carthage University (in 2017). He subsequently held his first postdoctoral position at the Natural Resources Institute Finland (LUKE) (2018-2021). His postdoc research focused on developing novel genomic reliability approximation methods in SNP-BLUP and ssGBLUP models for large dairy cattle evaluation. He started his second postdoctoral fellowship at the University of Vermont (UVM) in 2022 under the supervision of Drs. Stephanie McKay (UVM) and Curt Van Tassell (USDA). His current research focuses on generating genetic merit predictions using existing data from infra-red spectroscopy data of milk components, and on integrating high-throughput phenotyping derived from existing data and available genotyping data to enable genomic selection and genome wide association studies for economically important traits in dairy cattle.

The increasing availability of genomic information for nearly all livestock species has led to growing interest in using this information to help to select the best animals in genetic improvement programs. Genomic selection has been associated with additional accuracy in estimating breeding values and reduction in generation intervals, which has led to higher and faster genetic gains. However, the genomic response may differ depending on the species and the population structure, and the genomic strategy may still be unclear in some cases. This is why he wishes to pursue his professional career in the field of genetic and genomic evaluations and focus on improving livestock production using genomic information. He is interested in statistical learning-based approaches and in the development of genomic breeding value prediction models for dairy cattle with linear and non-linear models. He is also interested in the statistical models for gene mapping and the gene mapping methods.

Advisor: Stephanie McKay