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Background and Research Interests
As a PhD fellow supervised by Prof. Weddigen, I am currently working with digitized art historical collections with image and text components and metadata. In accordance with Digital Visual Studies and the research areas of visual and textual computing as well as spatial analyses in terms of layouts, my project centers around image-text compositions. I elaborated a research concept combining automated research for digital collections with art historical and linguistic research interests while staying at the Bibliotheca Hertziana to familiarize myself with the collections, Digital Humanities endeavors, and art historical concepts.
During my B.A. with a specialization in linguistics and media studies, including studies of multimodality, I became aware of the potentials of quantitative and computational approaches to complex data, as well as definition entanglements and limitations that can come along with their manual as well as automatic analyses. For this reason, I decided to study an M.Sc. in computer science to gain a better foundation in programming and algorithmic thinking and specialized in media informatics and AI and Machine Learning. Before joining DVS, I worked as a Software developer for media digitization workflows and as a research assistant in the library of the Max-Planck Institute for Mathematics in the Sciences, where I developed computer vision applications for scanned documents. Among others this included Document Layout Analysis.
My current focus lies in computational humanities and automated research on digitized art historical collections. As a starting point I am studying early modern emblems, which are especially interesting for the study of multimodality and iconography. They offer an allegorical composition of text – a motto and an epigram – with a composed image. As for my PhD project I am working with visual and textual parts of the data and relations to different levels of metadata and temporal and spatial diversion. With this project I would like gain statistical insights on common associations, feature roles and their collection-wise and historical distributions.