Published multiple research papers on recommender systems, conversational agents, and evaluation frameworks. Notable works include 'Mitigating Misleadingness in LLM-Generated Natural Language Explanations for Recommender Systems: Ensuring Broad Truthfulness Through Factuality and Faithfulness' and 'What Is Serendipity? An Interview Study to Conceptualize Experienced Serendipity in Recommender Systems.' Also contributed to the 'Conversational Agents: A Framework for Evaluation (CAFE): Manifesto from Dagstuhl Perspectives Workshop 24352.'
Research Experience
Developed RecPack, an experimentation toolkit for top-N recommendation using implicit feedback data. Participated in a project measuring filter bubbles in online news, using Generalized Linear Mixed-Effects Model (GLMM) for data analysis.
Education
Graduated from the University of Antwerp in 2024, focusing on methodologies to evaluate recommender systems.
Background
Interdisciplinary researcher who is genuinely passionate about fostering diverse exposure and discovery through algorithmic recommendation. Creative problem solver and team player, with a love for cross-disciplinary collaboration.
Miscellany
Enjoys finding fun in work and turning jobs into games.