I study algorithmic infrastructures, including generative AI, as socio-technical systems, from their construction and evaluation to their adoption and governance, and the ways they reshape institutions and everyday life.
I am currently examining how digital sovereignty can be operationalized and made more legible in the EU to enable autonomy that promotes social and public values. My interdisciplinary approach is informed by sensibilities from science and technology studies and technical expertise in computer science. I use both qualitative and quantitative methods and draw on literature from infrastructure and media studies, human-computer interaction, and social studies of algorithms, AI, and data.
Research
AI Evaluation Infrastructures
Rethinking how AI systems are tested, benchmarked, and trusted.
Digital Dependency & Sovereignty
Understanding digital dependencies and paths toward autonomy.
Automated Decision-Making
Algorithmic profiling and risk prediction in public and private sectors.
Platforms & Online Safety
People's perspectives on online harms, remedies, and data practices.
Publications
I have published academic articles examining socio-technical aspects of algorithmic systems on topics such as unrest prediction, algorithmic profiling in public welfare, and emotion recognition. I also examined globalized testing infrastructures for generative AI, how conceptions of algorithmic bias matter in public discourse, and how online harassment on social media can be curbed.
My work has appeared at academic venues such as ACM SIGCHI (Human-Computer Interaction), ACM FAccT (Fairness, Accountability, and Transparency), ACM CSCW (Computer Supported Cooperative Work & Social Computing), Big Data & Society, and Frontiers in Big Data. A full list is on the publications page.
Background and education
I hold a PhD in information with a specialization in science and technology studies from the University of Michigan, advised by Christian Sandvig and Silvia Lindtner, and a master’s degree in computer science (Logic and Computation) from TU Wien.
I have also previously been affiliated with the Center for Ethics, Society, and Computing (ESC) at the University of Michigan, the Austrian Academy of Sciences, the National Institute of Informatics (NII) in Tokyo, and TU Wien.
Impact
My work was featured in US and European media outlets such as Wired, ORF, Ö1, and Der Standard. My research on algorithmic profiling and online harms has been cited in parliamentary research service reports for the European Parliament, the German Bundestag, and the Austrian Parliament, as well as by Human Rights Watch.
I presented my work at conferences such as the Society for the Social Studies of Science (4S), the Association of Internet Researchers (AoIR), and the Society for the Advancement of Socio-Economics (SASE), and at events including the Information Society Project’s Data (Re)Makes the World conference at Yale Law School, the Graduate Research in STS (GRiSTS) conference at Harvard University, and the Oxford Media Policy Summer Institute at the University of Oxford.
Teaching
My teaching experience spans 19 courses across computing, human-computer interaction, algorithms and society, and qualitative methods.
At IT:U, I am an instructor for the course Interdisciplinary Seminar, which focuses on collaboration, research communication, and peer review. At TU Wien, I co-developed and taught a seminar on critical algorithm studies and taught in Welcome.TU.code, a lecture series for unaccompanied minor refugees. At the University of Michigan, I was a graduate student instructor for courses ranging from programming, databases, and information visualization to data science ethics and algorithms and society.
I have also mentored a research assistant and served as a PhD peer mentor. An overview of my courses is on the teaching page.