Havva Ebrahimi Pour

Havva holds a Bachelor’s degree in Political Science and a Master’s degree in International Relations. She gained valuable research experience in examining the impact of social media on new social movements, which sparked her curiosity to investigate further the complex interplay between data, media, and various socio-political factors. To gain a deeper understanding of European Union institutions and their policy-making processes, she pursued a second master’s program at Vrije Universiteit Brussel (VUB) in Political Science, specialising in European and International Governance. Her interest in data science was piqued during her Master’s thesis research, where she investigated the role of artificial intelligence(AI) and big data in policy domains such as migration management. She then undertook an intensive AI Developer traineeship, gaining hands-on experience with data management and AI tools. Following that, she worked as a data scientist at a company specialising in cultural and leisure communication, where she applied her skills and knowledge to real-world problems.

Her academic background and research experience, coupled with her knowledge of data science, led her to SMIT, where she is a researcher at the “Digital Inclusion and Citizen Engagement” unit. Her goal is to investigate building a more inclusive citizen engagement and participatory democracy by developing more innovative methods to ensure that diverse voices are heard, governments are more accountable, and have a smart and resilient future.

Havva is involved in several projects, including the  EU-funded project CLIMAS is an EU-funded projects. Her role in the CLIMAS involves presenting guidelines and methodologies for citizen-collaborative future scenario-building and using data science for transparent decision support to make an informed choice on the preferred scenarios. Additionally, she is involved in projects that aim to improve the decision-making process based on structured techniques such as the Analytic Hierarchy Process (AHP).