Dr. Hans Marvin studied chemistry at Free University of Amsterdam (VU) and received his MSc in 1985. Afterwards, he studied Biotechnology at University of Groningen and received his PhD in 1988. Following a few Post-doc positions, he started as senior scientist (biochemistry) at Centre for Plant Breeding and Reproduction Research (CPRO) in Wageningen, the Netherlands in 1991, leading the section Quality Improvement.
In 1999, he moved to RIKILT Wageningen UR, now called Wageningen Food Safety Research (WFSR). At present, Dr. Hans Marvin is a senior scientist specialized in many aspects of food safety, including analysis of foods, research on safety, and risk assessment for authorities. He works on a number of food safety issues, including emerging risk, big data and Artificial Intelligence (AI), food/ feed issues related to biotechnology and nanotechnology and risk analysis and has initiated various activities within the Netherlands and EU in these fields. Dr Marvin’s personal research specialisms are (i) methods for early warning & emerging risk identification, (ii) application of a system approach to food safety and modelling, (iii) application of Bayesian Networks in prediction models for food safety and food fraud, iv) introduction of big data analytics in food safety research including big data infrastructure. On these topics he has organized and chaired numerous workshops and is author and co-author of over 100 peer-reviewed scientific publications.
Augmenting human minds: artificial intelligence and big data in risk assessmentSee more
22/06 - 09:00Visit the agenda
Title of talk
Opportunities of Big Data and AI in Food Safety Risk Assessment
22/06 - 09:10
Abstract of talk
The EU Farm-to-Fork strategy refers to One Health as an approach for tackling emerging issues, and underlines the need for holistic transdisciplinary approaches to move towards safe and sustainable food systems. Sustainable food systems are embedded in a complex web of interacting drivers from within and outside those systems, influencing food supply chain performance both locally and globally. These drivers include: (i) a growing world population and changing diets; (ii) climate change; (iii) diminishing biodiversity; (iv) governance; (v) (competing) agricultural systems; (vi) market structure, economic development & financial structures; (vii) urbanisation; (viii) policies; (ix) technology & innovation; (x) consumer behaviours, etc. Understanding the complexity of the food supply chain, including the drivers and their interrelationships, is key in developing methodologies to transform the EU food system (‘from Farm-to-Fork’, producing a more sustainable, safe and healthy diet). A systems-based approach that takes this web of indicators into account is therefore required, supported by digital innovations (artificial intelligence and big data technologies). This presentation will show examples of the implementation of systems-based approaches, with the development of prediction models for food safety and food fraud, in which expert consultations and AI play a key role. In particular, the use of Bayesian Networks has shown promising results. Examples of the application of AI algorithms to media text and scientific literature will also be given; the focus is on collecting, processing and visualising data as an intelligence activity to improve food safety control. Lastly, examples will be given of novel technologies developed to screen food samples online, or used onsite for food safety risks, using big data and AI. It is concluded that big data in food safety is not yet being fully applied and that big data infrastructures and acceptable data governance methodologies (security, ownership etc.) are needed.