Speaker
Nicolo Pagan
Nicolo Pagan is a Postdoctoral Researcher at the Social Computing Group at the University of Zürich, working with Prof. Anikó Hannák. Since January 2022, he has also been a member of the National Centre of Competence in Research «Dependable, ubiquitous automation» (NCCR Automation). In April 2021, he completed his doctoral degree at the Automatic Control Lab at ETH Zürich under the supervision of Prof. Florian Dörfler. Prior to that, from 2013 to 2015, he was a Software Engineer and Research consultant at Ascomp AG.
Nicolò’s primary research interests lie in the realm of AI ethics, where he explores the complex interplay between technology and human behavior in an intricate network system. Through his expertise in mathematical modeling, complex systems analysis, and data science, he is able to delve into this field of study with a unique perspective and he aims to contribute valuable insights to the ongoing discourse on the ethical implications of AI and technology in our rapidly evolving digital landscape.
More precisely, his current research revolves around two pillars:
the long-term fairness effect of feedback loops engendered by automated Machine Learning-based decision-making systems. the use of generative AI, based on Large Language Models (LLMs), for building large-scale human environments for testing health notification interventions. In his previous work, he investigated the impact of recommender systems within social media platforms, with a particular emphasis on discerning how these systems influence the fairness of the network formation processes. Two of his recent works have been published in Nature Communications.
More broadly speaking, Nicolò is interested in Social and Engineering Systems and in problems that correspond to significant societal and ethical challenges, with an emphasis on areas such as autonomous systems, energy systems, finance, social networks, urban systems, and their underlying interconnections.
More Information:
Talks at this conference:
| 13:30 | Validation metrics and approaches of GABMs for social media research Live |