József Baranyi is a Hungarian – British mathematician, who received his PhD in Applied Mathematics from the University of Szeged, Hungary. He worked for the Institute of Food Research in the United Kingdom for 26 years, leading the Computational Microbiology Research Group there. He was also a Visiting Professor at the Physics Department of Imperial College in London through 2012-2019. In 2016 he retired in the UK and became a Scientific Advisor at the University of Debrecen, in his native Hungary, where he led the Predictive Nutrition Research Group. In 2025, he became the ERA Chair Holder of the foodigIT Computational Food Science Centre at the Aristotle University of Thessaloniki, Greece.
His research focuses on “How to make sense of data” in food sciences, primarily in food microbiology. He authored or co-authored more than 100 research papers, book chapters and other scientific communications with >8000 citations (Scopus, 2025). He spent several months, multiple times, in various international research establishments, such as the Food Science Division of CSIRO, Australia; the University of Bologna, Italy; the Eastern Regional Research Centre of the USDA-ARS; or the University of Notre Dame, South Bend, IN. He has coordinated or participated in several UK, EU and other international projects. He was a founding member of the “ComBase” system (www.combase.cc), which is built on the ontology he had developed and the mathematical model that bears his name.
He was the Statistical Advisor of the Journal of Applied Microbiology for 14 years and Editorial Board member of the Applied and Environmental Microbiology for 15 years, for which he received the “Distinguished Service Award” of the American Society for Microbiology. Currently he is an Editorial Board member of the International Journal of Food Microbiology. He is also an elected member of the International Academy of Food Science and Technology, the prime advisory body of the International Union of Food Science and Technology.
Title of presentation : Computational Food Science – Making sense of food data