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Digital Visual Studies

Dr. Eva Cetinić

Imagegraph DVS Postodoctoral Fellowship

Dr. Eva Cetinić , Postdoctoral Fellow, from September 2021, Ph.D. in Computer Science, Faculty of Electrical Engineering and Computing, University of Zagreb 2020.

Eva Cetinic joined DVS in September 2021 as DVS Imagegraph Postdoctoral Fellow. Before, she worked as a postdoctoral research associate in Digital Humanities and Machine Learning at the Department of Computer Science, Durham University. She obtained her Ph.D. in Computer Science from the Faculty of Electrical Engineering and Computing of the University of Zagreb in 2019 and worked as a professional associate and postdoc from 2015 to 2021 at the Rudjer Boskovic Institute, Croatia. Her research interests focus on exploring new research possibilities rooted in the intersection of Artificial intelligence and Art History. She joined DVS to work on the improvement of the existing framework, a web-based, open-source prototyping toolkit for Data Science in the Arts, Humanities, and Social Sciences. In collaboration with Dr. Leonardo Impett, she will work on the functional extensions of the current framework with deep and machine learning methods, as well as on the development of research use-cases for ImageGraph, based on textual and/or visual datasets mainly from the field of Art History. Apart from the involvement in the Imagegraph project, she is planning to collaborate with other members of the DVS team in pursuing her research interests and future projects. In her previous research activities, she was mainly preoccupied with the topic of how computer vision and deep learning methods can be used for computational analysis of fine art images. Recently, she became more interested in multimodal deep learning and how those methods can be used to study various modalities of data, e.g. image and text, in a joint semantic feature space. She is particularly interested to explore how advanced methods from this field can be applied to study multimodal representations in artwork collections, and what new applications and research directions can emerge from such approaches.