Optimisation and data analytics for innovative industry solutions
Professor Regina Berretta has long been interested in the crossover of the disciplines of computer science, mathematics applied to a diverse number of fields.
With a degree in Computational and Applied Mathematics and a Master and PhD in the area of metaheuristic methods for addressing integer programming problems, Professor Berretta's research interests include the design of mathematical models and the development of efficient computation techniques to tackle large and complex combinatorial optimisation problems, in a variety of application areas, like bioinformatics, supply chain optimisation, production planning, timetabling problems, among many others.
Currently a Chief Investigator at the ARC Training Centre for Food and Beverage Supply Chain Optimisation, Professor Berretta's expertise in metaheuristics and integer programming mathematical models are being used in a diverse number of problems in different food industries, as food loss minimisation, inventory optimisation, lot sizing and scheduling optimisation, among others. This Centre has received over $2 million in funding and will train the next generation of multidisciplinary researchers capable of designing, building, and managing these supply chains. Determining the best trade-off between the associated costs and the benefits to optimise the total profit is a critical problem facing fresh-product producers and distributors.
Professor Berretta was a founding member of the former Priority Research Centre for Bioinformatics, Biomarker Discovery and Information Based Medicine (CIBM) for ten years. In CIBM, her expertise in large scale optimisation problems has driven a multidisciplinary team that specialises in large-scale data analytics. The Centre has excelled in delivering novel, scientifically insightful, mathematically-literate, supercomputing-based processes for exploring and interpreting the massive datasets now being generated by modern society's deluge of information.
By combining multiple datasets, richer, more insightful relationships can be uncovered that would otherwise remain hidden when analysing the individual datasets alone. These techniques have wide application such as developing targeted therapies for diseases, generated by isolating and identifying the metabolic pathways employed by the disease, or to develop more robust understanding of regulatory networks by comparing networks across pathologies and species.
If her recent output is anything to go by, Berretta has established a strong reputation in a diverse number of multidisciplinary fields involving the application of optimisation problems. She has co-authored more than 80 papers and book chapters, presented at international conferences and received over $4 million in funding through 35 grants.
Professor Berretta knows well that computer science and complex mathematics have an ever-increasing role to play in the decision making in a diverse number of fields, like manufacturing, health research, transport, data analytics, etc. She is inspired to continue to combine her expertise to furthering the exciting and fulfilling field of bioinformatics and innovative solutions for the industry.
Women in STEM
Prof Berretta is a co-founder of HunterWISE, a group dedicated to promoting and supporting girls and women in STEM. HunterWISE features two interlinked actions aimed at increasing the number of girls and women participating in STEM through a school program and a series of networking events across the Hunter for women STEM professionals. This approach is designed to steer women toward STEM, and encourage their retention in the STEM pipeline.
Google – CS4HS
Prof Berretta is also one of the leaders of a project funded by Google (GoogleCS4HS) in five successful consecutive years, which consists of implementing and supporting a series of workshops of which the objective is to educate, inform and prepare high school teachers in the Hunter region to engage and inspire their students in the wonders of Computer Science and its applications.