I am Assistant Professor of Cultural Data Analysis based in the Department of Media Studies and the Institute for Logic, Language, and Computation. I integrate computer vision, semantic technologies, and media and queer theory to both critically examine and technically develop AI systems used in multimodal cultural analytics.

My research explores how abstract social concepts—such as identity, vulnerability, and toxicity—are operationalized in, and shaped by, computational logic in the era of AI. I am guided by concepts like glitch and alienation.

Overall, I challenge and play with assumed binaries, like public/private, nature/culture, male/female, and white/colored. Overall, I am committed to producing knowledge that explores how human and non-human agents adapt within the dynamic interplay of technological advancement, societal values, and cultural diversity.  My overall aim is to seek new ways to think about power, representation, and technological change.

​Previously, I was a post-doctoral researcher at the Human-Centered Data-Analytics (HCDA) group at Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands. I have a Ph.D. in Computer Science and Engineering and a M.A. in Digital Humanities and Digital Knowledge from Università di Bologna (Italy). I have a Bachelor's degree in Human Evolutionary Biology, with a minor in Gender and Sexuality Studies, from Harvard University (U.S.A.).

Specifically, my current research directions include the following:

Identity Labeling in Latent Spaces

• Investigating how (Gen)AI models encode, learn, and map identity concepts within latent space and analyzing stereotypical representations vs. the "unrepresentable," with a special emphasis on synthetic images, edge cases, and algorithmic glitches.

AI and the Precarization of Vulnerability

• Framing data research as an active act of "world-making" rather than passive observation by examining how researchers design data pipelines that transform “vulnerable" individuals into data subjects whose vulnerability can be further precarized.

Memetic Toxicities

• Exploring the intersection of toxicity and memetics in digital spaces by analyzing what makes toxic content viral/memetic (and vice versa), evaluating AI models used to detect, operationalize, and analyze online toxicity, and mapping cross-platform dynamics and content propagation.