Dr. Cork is a seasoned food scientist with extensive experience in the food processing industry. He began his career with an undergraduate degree in chemistry, specializing in natural products, from Macquarie University, where he graduated with honours. Over the next 15 years, Dr. Cork honed his skills in various roles, including managing a NATA certified food and water laboratory at an integrated feedlot export abattoir, teaching laboratory operations at NSW TAFE, and serving as a technical officer with DPI on rice fragrance. He also worked as a QA consultant for the NSW EPA soils lab and as a quality manager at a greenfield canola oil factory, while actively participating in the Australian Oilseed Federation.

Dr. Cork's field is food processing research, and he is passionate about expanding the scope, capability, and complexity of food processing from the Riverina region to the global stage. His research and professional experience span both the grain and meat processing sectors. In grain processing, he earned his PhD in Food Science and Biotechnology from Charles Sturt University, as part of the Functional Grains Centre, where he focused on value addition to grain pulses through methods such as flaking, extrusion, and protein extraction. He has conducted laboratory evaluations of a wide range of food characteristics and quality parameters for grains and meat, ensuring industry standards for quality and food safety are met. Dr. Cork's recent work includes the rapid detection of bovine respiratory disease pathogens using real-time PCR in calves.

The purpose of Dr. Cork's current research is to facilitate the implementation of machine learning vision systems into meat processing systems. His work aims to demonstrate the capability of smart imaging systems to improve the quality and capacity of meat inspection processes, particularly in assessing offal and meat quality. His postdoctoral research, funded by Meat and Livestock Australia and industry partners, is centered on developing automated systems for identifying offal defects using hyperspectral imaging technology.