I am interested in both contributing to the AI state of the art and to the advancement of the United Nations Sustainable Development Goals, with special focus on poverty and inequality mitigation. I enjoy generating proof of concept of my research projects and making them scalable with the participation of multidisciplinary research groups.

I am currently working in the following projects. 

·   International:


o   NOA Poverty Project (Norm Optimization Approach for Poverty Mitigation). Inform a new path for poverty mitigation by providing simulations to foresee the impact of alternative policies (pilot project and expansion to a network of cities (with Montes, N., Osman, N., Sierra, C.). Award-winning project at IJCAI’22.


o   Define and implement an Index for Poverty Bias to periodically report the national and global levels of discrimination against the poor to the United Nations (UNDP), national governments and international NGOs (with Kiritchenko,S.,Nejadgholi,I., Fraser, K.C., from the National Research Council of Canada).


o   Creation of an AI for Good Syposium in Africa, under the umbrella of the International Joint Conference of AI (together with the other AI and Social Good co-chairs).


·    National (US):


o   AI for Good credit scores. Generate a credit score for refugees to facilitate support services for the most vulnerable families and mitigate credit defaults (in collaboration with the US Conference of Catholic Bishops).


o   Bridging social network. Implementation of social cohesion algorithms to facilitate the connection between refugees and the US population in a specifically created social network (in coordination with USCCB).


·    Local:


o    Generate an AI for Good participatory set of activities in collaboration with local community (pilot case conducted in Berkeley public libraries). Explain the positive contributions of AI towards the UN Sustainable Development Goals, identify local needs and implement a pilot project where the community members actively participate in the AI fairness decicison making process.