Indira Sen

Indira Sen is a postdoctoral researcher and lecturer at the University of Konstanz in the Department of Politics and Public Policy. Her work bridges computational methods and social science, focusing on the development of robust, theory-driven techniques for measuring social constructs from digital trace data.

Education:

• PhD in Computational Social Science, RWTH Aachen and GESIS Leibniz Institute for the Social Sciences, advised by Prof. Claudia Wagner and Prof. Wil van der Aalst
• MSc in Computer Science, IIIT Delhi, advised by Prof. Ponnurangam Kumaraguru at PreCog
• BSc in Computer Science, Vellore Institute of Technology

Her research integrates natural language processing and measurement theory to enhance the accuracy, transparency, and fairness of insights derived from digital behavioral data. She investigates how computational models, particularly large language models, represent diverse cultures and languages, highlighting issues of misrepresentation and bias. Her work is highly relevant for policymakers and researchers relying on digital data for social analysis.

The available publication indicates a strong focus on cultural and linguistic bias in AI-driven social simulations, suggesting a research trajectory centered on ethical and methodological rigor in computational social science.

Scientific Awards:

• No awards mentioned in the provided text.

Indira Sen has served as a lecturer and mentor during her postdoctoral tenure, contributing to academic training in computational social science. While specific grants are not listed, her research at the intersection of social science and AI likely involves funding from interdisciplinary or digital society initiatives. She presented her work on multicultural misrepresentation in LLMs at the Complexity Science Hub in August 2024, reflecting active engagement in the scientific community.

She is associated with research teams at the University of Konstanz and previously with PreCog at IIIT Delhi and the Computational Social Science group at GESIS. Her collaborative work with prominent scholars suggests integration into leading networks in computational social science.