Judith and David Coffey Seminar Series: Hayes

Title: Can recent advances in machine learning help feed the world? 

Speaker: Professor Ben Hayes (University of Queensland) 

Abstract: Machine learning has enabled step changes in progress in some fields recently,
most notably in predicting 3D structure of proteins and generative text on very diverse
topics.  These applications use deep learning models trained on colossal data sets to
make their predictions.  Crop and livestock breeders are now using phenotypic, genomic
and omic data sets with billions of data points to breed higher yielding, more adapted
varieties and animals.  Given this explosion of data, and the ability of machine
learning to analyse large data sets, it would seem useful to explore how machine
learning can assist crop and livestock breeders.  in this presentation, the potential
application of machine learning to contribute to crop and livestock tasks is assessed
and compared to existing methods.  The conclusion is that for some tasks, machine
learning could make a major contribution, for other tasks, existing methods outcompete
machine learning methods.  

About the speaker: Professor Hayes has extensive research experience in genetic
improvement of livestock, crop, pasture and aquaculture species, with a focus on
integration of genomic information into breeding programs, including leading many large
scale projects which have successfully implemented genomic technologies in livestock and
cropping industries.  Author of more than 300 journal papers, including in Nature
Genetics, Nature Reviews Genetics, and Science, contributing to statistical methodology
for genomic, microbiome and metagenomic profile predictions, quantitative genetics
including knowledge of genetic mechanisms underlying complex traits, and development of
bioinformatics pipelines for sequence analysis.  Highly cited researcher 2015 - 2022.