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Ákos Jóźwiak DVM, PhD

research director

Digital Food Chain Education, Research,
Development and Innovation Institute

University of Veterinary Medicine, Budapest

Ákos Jóźwiak is the research director of the Digital Food Chain Education, Research, Development and Innovation Institute at the University of Veterinary Medicine, Budapest. Before, he worked for the National Food Chain Safety Office (NÉBIH) and its predecessors for 15 years in various positions: he was the head of the National Reference Laboratories, then worked on planning of the risk based sampling and control plans, and was responsible for drafting the Food Chain Safety Strategy 2013-2022 of Hungary. In the recent years he worked in the area of strategic planning, food safety risk assessment and data analysis.

In his international relation activities, he is a member of the EFSA Advisory Forum and the EFSA Emerging Risk Exchange Network. Through these networks, he participated in many projects and initiatives; and he chairs the EFSA Advisory Forum Data Collection and Modelling Task Force. The objective of the Task Force is to overview the data collection and reporting processes, the data models and IT infrastructure used in the sharing of data between data providers and EFSA, and to formulate recommendations at a strategic level.

Besides, he participated in many international capacity building projects as junior project leader, component leader and expert. He also participates to Better Training for Safer Food (BTSF) initiative as a tutor and leads various university courses. He is the chair of the Hungarian Committee of the Codex Committee on Methods of Analysis and Sampling (CCMAS). This FAO-WHO Committee deals with laboratory measurement related questions, providing an access to an international network of food laboratory scientists.

In his research activities, he focuses on developing and applying new analytical methods for improving the effectiveness of the food chain safety controls. Within this domain, the main research areas are (1) applying computational science methods for determining emerging risks and for improving risk-based controls; (2) epidemiological simulation of animal diseases; (3) determining the economic burden of foodborne diseases and applying health technology assessment methods for food chain safety decision making.

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