Team

Dr. Eva Cetinić’s scientific interests focus on the challenges of interdisciplinary research at the intersection of deep learning, explainable AI, and digital arts and humanities. After receiving the Ambizione research grant for the project she acts as project lead exproring deep learning techniques for computational image understanding and multimodal learning in visual art and culture. Her current research investigates the cultural, ethical, and societal implications of emerging multimodal foundation models, particularly how text-to-image models encode socio-cultural patterns and impact artistic and cultural production and appreciation.

Dr. Cetinić received her Ph.D. in Computer Science from the University of Zagreb in 2019, where she studied computer vision and deep learning for analyzing large collections of fine art images. From 2015 to 2021, she worked at the Rudjer Boskovic Institute in Croatia on international projects for the DARIAH research community. Before joining Digital Society initiative in 2023, she was a postdoctoral researcher at Durham University and the University of Zurich.

Since February 2024, Maria-Teresa is a PhD Student at the Digital Society Initiative of the University of Zurich. She holds a Bachelor’s degree in English Language and Literature from the University of Athens and a Master’s degree in Computational Linguistics from the University of Konstanz.

Her current research, under the guidance of Dr. Eva Cetinić, seeks to unravel the diverse cultural, ethical, and societal implications of advancing multimodal deep learning models. Specifically, her work focuses on examining the explainability and potential biases of these models, alongside their capabilities in risk mitigation, cultural analysis, and artistic exploration.

Laura is a former project member of the Research project KITeGG with a background in interdisciplinary design. Since 2024 pursues her PhD at University of Zurich UZH. Her research is embedded within the project The Canon of Latent Spaces: How Large AI Models Encode Art and Culture and specifically focuses on investigating the implications of creating and sharing personalized, open-source text-to-image/video generative AI models.

This research effort aims to identify how broader systemic issues, such as the practice of appropriating unlicensed and/or violent material in open-source multi-modal AI systems, cascades into the communities that adopt, modify and share them.