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Scope and aim

Food systems have become very complex as they embrace a range of actors and their interlinked value-adding activities related not only to food, but also to human and animal health and ecology, as well as to economy, science and innovation. Formalization of concepts describing food systems being integrated in such a wide ecosystem requires deep understanding of their building blocks and linkages among them, which can be achieved by the exploration and exploitation of big data carrying valuable information. Recent advances in the collection, normalization, linkage, analysis and visualization of big data on food, nutrition and environment have enabled structuring of related knowledge, which opens novel decision-making possibilities. In particular, this has proven to be important during the recent pandemics of COVID-19 and other catastrophic situations, when food provision and security are threatened.

The BFNDMA workshop has established a global forum for discovering, discussing and communicating fresh ideas, advanced methodologies and data-driven solutions for dealing with big data related to food, nutrition, and environment. Its main aim is to enable better understanding and improvement of food systems as required by both society and environment. The BFNDMA 2022 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).

The focus of BFNDMA is 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 BFNDMA 2022 will consider original and unpublished research articles that propose bold steps towards addressing the challenges of data management and analysis for food, nutrition and environmental data, with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 relations 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.

Invited Speakers

Aleksandra Kovachev, PhD

Data Science Manager, Delivery Hero SE

Short Bio: Dr. Aleksandra Kovachev is Data Science Manager at Delivery Hero, one of the world’s leading online food ordering and delivery marketplaces and a European startup unicorn. With her teams of multi-disciplinary Data Scientists, her mission is two fold: build world catalogue of dishes and customer taste profiles using state-of-the art Machine Learning models, but also build and provide global service for efficient and reliable customers Identity Resolution. She has been in the field since 2016, working in range of topics: from Bioinformatics and Personalised Medicine to Recommendations & Ranking. Before that, she completed a Ph.D. in Computer Science and Engineering in Skopje, with focus on Complex Networks and their applications in Protein-Interaction Networks, Multiplex Networks and Consensus and Synchronisation. Today on daily basis she has the opportunity to mix interdisciplinary research with direct industry impact.

Joan Capdevila Pujol, PhD

COVID Data Science Team Lead, ZOE

Short Bio: Dr. Joan Capdevila Pujol leads the Research Data Science team at ZOE, the AI-first nutritional science start-up behind the PREDICT programme and the ZOE Health Study (formerly known as COVID Symptom Study). By leveraging remote data acquisition, ZOE is collecting large-scale, high-quality and precision datasets across a breadth of exposures and outcomes, that are advancing nutritional and health research at an unprecedented pace. Joan is thus at the forefront of developments in nutritional research with his innovative analytical approaches within the PREDICT programme dataset; the world’s largest ongoing nutritional research programme. His team is behind many research papers in leading medical journals related to both Nutrition and COVID-19. Further he led the fast-paced Data Science team that were instrumental in delivering real time, actionable findings in relation to COVID-19 symptoms, exposures and outcomes which have been disseminated internationally through media (printed press, radio and television). Before his time at ZOE, he completed a PhD in Machine Learning from Polytechnic University of Catalonia with a focus on Natural Language Processing and probabilistic modelling.

Prof Chris Evelo, PhD

Department of Bioinformatics, Maastricht University, the Netherlands

Short Bio: Chris Evelo is the founding head of the Department of Bioinformatics - BiGCaT at Maastricht University, where he leads an enthusiastic group of researchers and he is a PI in the Maastricht Center for Systems Biology (MaCSBio). His current research focuses on bioinformatics for integrative systems biology; aiming at a better interpretation of experimental data through integration in data models that build on structuring existing knowledge. Chris is scientific advisor for the IMI project for translational quantitative systems biology (TransQST). He also leads the systems biology work package in the European Joint Project for Rare Diseases, and a task on mappings between ontological concepts and database identifiers in the IMI project FAIRplus. In the nutrition domain, he is part of the management team of the nutrigenomics organization NuGO and partner in the Food Nutrition Security Cloud (FNS-cloud) project. He helped conceptualize and develop the phenotype database. This works aims at using the overarching European research infrastructure including core data resources and deposition databases with specific adaptations to allow capturing and analysis of data from nutritional and more in general metabolic studies including microbiome.

Babak Ravandi, PhD

Alexion, AstraZeneca's Rare Disease, USA

Short Bio: Dr. Babak Ravandi received his PhD from Purdue University in Computer and Information Technology focused on the controllability of complex networks. He then began his postdoc at the Center for Complex Network Research at Northeastern University, Boston to further study complex systems, where he developed tools and databases to measure the degree of food processing for any food in grocery stores available at http://TrueFood.Tech. Currently, he is working at Alexion, AstraZeneca's Rare Disease to implement network biology tools.


Please submit a full-length paper (up to 10 page IEEE 2-column format), or short or position paper (2-4 page IEEE 2-column format), through the online system.
Papers should be formatted following the IEEE Computer Society Proceedings Manuscript Formatting Guidelines, as instructed by the conference organizers.

Manuscript templates
Microsoft Word
LaTex Formatting Macros

NOTE: In order to participate to this workshop, full or student registration of IEEE BigData 2022 is required for at least one of the authors.

Important dates

  • Paper submission - EXTENDED: Oct 15, 2022
  • Decision notification: Nov 8, 2022
  • Camera-ready submission: Nov 27, 2022
  • BFNDMA 2022 Workshop (online): Dec 19, 2022 - 14:00 - 18:00 (Program)


Prof. Barbara Koroušić Seljak, PhD

Computer Systems Department,

Jožef Stefan Institute, Ljubljana, Slovenia

Asst. Prof. Tome Eftimov, PhD

Computer Systems Department,

Jožef Stefan Institute, Ljubljana, Slovenia

Sarah Berry, PhD

King’s College London, UK

Gjorgjina Cenikj

Computer Systems Department,

Jožef Stefan Institute, Ljubljana, Slovenia

Gordana Ispirova

Computer Systems Department,

Jožef Stefan Institute, Ljubljana, Slovenia

Vladimir Kuzmanovski, PhD

Department of Computer Sciences,

Aalto University, Espoo, Finland

Giulia Menichetti, PhD

Center for Complex Networks Research (CCNR), Northeastern University, USA

Brigham and Women’s Hospital, Harvard Medical School, USA

Ana Nikolikj

Computer Systems Department,

Jožef Stefan Institute, Ljubljana, Slovenia

Eva Valenčič

School of Health Sciences, College of Health, Medicine and Wellbeing,

University of Newcastle, NSW, Australia

Jildau Bouwman, TNO, NL
Duccio Cavalieri, University of Florence, IT
Damion Dooley, Ontology Development Lead at Centre for Infectious Disease Genomics and One Health, Simon Fraser University, Canada
Laurette Dubé, McGill University, Montreal, Quebec, Canada
Fabio Mainardi, Nestle Institute of Health Science S.A, Lausanne, CH
Bibek Paudel, Center for Population Health Sciences, Stanford University, CA, USA
Petar Ristoski, IBM Research, CA, USA
Maria Traka, Quadram Institute Bioscience, UK