SCOPE AND AIM
In the last decades, a great amount of work has been done in predictive modelling of issues related to human and environment health. Resolution of issues related to healthcare is made possible by the existence of several available biomedical vocabularies and standards, which play a crucial role for understanding health information, together with a large amount of health data. However, focusing solely on healthcare data limits the potential benefits that our lives and societies could have from the rapid development of artificial intelligence (AI) and its enormous capabilities. As such, Lancet Planetary Health in 2019 noted that the focus of future improvements in our wellbeing and societies will depend on investigating the links between food systems, human health, and the environment. However, despite the large number of available resources and work done in the health and environmental domains, there is a lack of resources that can be utilized in the food and nutrition domain, as well as their interconnections. In particular, this is important during the current pandemics of COVID-19, when food provision and security, as well as healthy nutrition and environment, are tremendously needed for quick recovery and long-term sustainable development of our societies.

For the purpose of attaining human and societal wellbeing through advances in the field of artificial intelligence (AI), BFNDMA aims at establishing a global forum for discovering, discussing and communicating AI ideas, methodologies and data-driven solutions that share the common wellbeing goals. The BFNDMA 2021 is a continuation of the successful workshops BFNDMA 2019 organized at IEEE Big Data 2019 in Los Angeles, California, USA and BFNDMA 2020 organized at IEEE Big Data 2020 (virtual event).

Our aim is to primarily focus on methodologies for big data management and analysis of structured and unstructured food, nutrition and environmental data, as well as their synergies and trade-offs. BFNDMA 2021 will consider original and unpublished research articles that propose bold steps towards addressing the challenges of data management and analysis for food, nutrition and environmental data, with strong emphasis on exploring the relation between food systems, human health and environment.




TOPICS OF INTEREST RELATED TO FOOD AND NUTRITION
Information retrieval, information extraction, natural language processing techniques and artificial intelligence for food,nutrition and environmental sciences;
Food and nutrition data normalization;
Knowledge representation for food and nutrition;
Ontologies, vocabularies and ontology design patterns, with a focus on describing the modelling process for food,nutrition and environmental sciences;
Crowdsourcing task designs that have been used and can be (re)used for building resources such as gold standards for food, nutrition environmental data;
Predictive and descriptive modeling of food systems, including food production and demand-supply chains;
Modeling life on land and underlying ecological and biophysical phenomena from satellite images;
COVID-19 impact on food, nutrition, and environment, with regard to their security, diversity, safety and provision.

Organizers:
Tome Eftimov, PhD, Jožef Stefan Institute, Ljubljana, Slovenia
Vladimir Kuzmanovski, PhD, Aalto University, Espoo, Finland
Gjorgjina Cenikj, Jožef Stefan Institute, Ljubljana, Slovenia
Gordana Ispirova, Jožef Stefan Institute, Ljubljana, Slovenia
Prof. Barbara Koroušić Seljak, PhD, Jožef Stefan Institute, Ljubljana, Slovenia
Jaakko Hollmen, PhD, Stockholm University, Stockholm, Sweden