I am the technical lead of the Ferrero ALGO-NUTS project at the ISI Foundation. The aim of this project is to analyze food production and nutrition using the latest advances in Data Science, to fully capture the complexity of these fields, and advance towards a Sustainable Nutrition.
Sustainable Nutrition is a powerful concept that studies the environment, food production and nutrition through the lens of big data and artificial intelligence. This new, interconnected concept aims to be a driving force for new healthy, nutritious and sustainable food solutions for all. Humanity is facing a “triple dilemma”: to produce more food, ensure its nutritional adequacy, and avoid the unjustified expansion of cultivated lands at the expense of native environment. The complex issues cannot be solved in isolation. The need for a new approach is evident: one that optimizes health and nutritional outcomes, while, at the same time, is effective in restoring the key ecosystems and farming livelihoods on which humanity depends. This is what we call “Sustainable Nutrition”, a key driver to help fix our global food system. It is the only way for the future. We must act, now. And we must do it together. More information
I am currently a fellow of the AccelNet-Multinet project. Besides allowing me to strengthen my relationship with my peers in the US, I am also actively participating in the development of a research marathon, in the spirit of hackatons, that will take place on late July 2022.
The AccelNet-MultiNet program was launched as an NSF-supported, joint effort led by the Network Science Institutes of Northeastern University (NetSI) and Indiana University (IUNI) to expand the international community of scientific discovery and training focused around the exploration of multilayer network science. More information
I am an external member of the COVID-SHINE project jointly coordinated by the Universitat Pompeu Fabra and the Institute for Cross-Disciplinary Physics and Complex Systems. I collaborate with my experience working with COVID-19 data to bridge the gap between epidemic modeling and the analysis of the social determinants of health.
The COVID-SHINE project is a La Caixa Foundation project whose aim is to understand the spatio-temporal social determinants of health to improve agent-based modelling of recurrent COVID-19 outbreaks. The project has two general aims: (1) to enhance the ability of modelling strategies to predict the dynamics of recurrent outbreaks of COVID-19 by incorporating novel knowledge of the social determinants of health, and (2) to develop an integrated systems framework to help to inform policies, emphasizing the reduction of health inequalities. More information