Sergi Nadal receives an award for Scientific Research into Urban Challenges in the City of Barcelona 2020

  • Mar06

    The Barcelona City Council has delivered on Tuesday, 16 March, the Awards for Scientific Research into Urban Challenges in the City of Barcelona 2020 announced last October to fight the COVID-19 pandemic. The ceremony, broadcast on YouTube, took place in the Saló de Cent of the Barcelona City Council and was chaired by the mayor of the city, Ada Colau, together with the Deputy Mayor for Culture, Education, Science and Community, Joan Subirats, and the delegate of Municipal Policies for Science and Universities and president of the jury, Júlia Miralles de Imperial.

    Fifteen research projects were awarded out of a total of 65 that were submitted. All the proposals were prepared by members of the scientific community under the age of 40, as the call for proposals was made with the intention of stimulating and strengthening young talent in Barcelona. The response has been a success and, moreover, a female success, as both the participating projects and the 15 award winners (with 23 beneficiaries) have been created mainly by women. Sergi Nadal has been awarded one of the projects to work on " An Automatic Data Discovery Approach to Enhance Barcelona's Data Ecosystem".

    The project proposes a novel research line on Data Discovery that will democratize the access to (open) data. The proposal is twofold: (i) a flexible shared and accessible data hub, under the town council’s control, where private and public actors publish their datasets. For this, we propose to rely on the Open Data BCN dataset catalog. And, (ii) an innovative semi-automated Data Discovery approach to effectively cross disparate, heterogeneous and intersectoral data sources without needing to manually process the data. We will automatically scrutinize the datasets: i.e., their data, definition (or schema) and hidden relationships, to automatically profile the dataset. We plan to use Graph Neural Networks (GNNs), an advanced AI technique that generalizes the deep neural network model to exploit further aspects such as topology and connectivity. This is nowadays a hot research topic, which has not yet been explored in the context of Data Discovery.