by Remzi Celebi, Postdoctoral Researcher at Institute of Data Science at Maastricht University
Personalized nutrition aims to promote well-being and health through individualized healthy nutrition choices based on individual’s’ dietary habits, genetics, microbiota and disease. Personalized nutrition is also key to the goal of creating a healthy, sustainable and affordable food ecosystem.
In this talk, I will talk about the challenges to realizing personalized nutrition framework and solutions and what AI and Data Science can offer to address these challenges.
What drives you?
We need to build technologies and solutions through which nutrition can be personalized and give explanations of the reasons that a particular food or diet is recommended to individuals.
Why should the delegate attend your presentation?
The presentation should be of particular interest to those who are interested in the possible uses of AI and Data Science in developing technologies for personalized nutrition.
What emerging technologies/trends do you see as having the greatest potential in the short and long run?
In the short run, Deep Learning definitely will be a key technology to enabling personalized nutrition. In the long run, changes in producing and consuming data with FAIR principles in turn will affect the food we eat and produce.
What kind of impact do you expect them to have?
Create awareness about challenges and opportunities of personalized nutrition.
What are the barriers that might stand in the way?
The lack of relevant data and technologies.
Knowledge is power but harnessing knowledge brings full power.
About Remzi Celebi
Remzi Celebi is a Postdoctoral Researcher at Institute of Data Science at Maastricht University. He completed his PhD from Ege University, Turkey in 2018 for his thesis entitled “Machine Learning based Semantic Link Prediction for Biomedical Knowledge Graphs”. His research interests include Machine Learning, Knowledge Graphs, Linked Open Data and Semantic Web Technologies. He has gained considerable experience in data integration and knowledge discovery from structured and unstructured data. His current research focuses on integrating multiple omics data into knowledge graphs and developing machine learning for personalized nutrition and medicine.
Institute of Data Science at Maastricht University (IDS) aims to foster collaboration for multi-disciplinary research, interdisciplinary education, and data-driven innovation. IDS’ research interests are threefold: accelerating scientific research through the development of powerful AI methods and community platforms; improving health and wellbeing informed by data-driven decision making, and empowering communities to use data science to address important and urgent problems that affect their quality of life.