Aristides Gionis

Professor at KTH Royal Institute of Technology, Sweden

Prof. Aristides Gionis is a WASP professor at KTH Royal Institute of Technology. He works in algorithms, data mining, graph mining, and social-network analysis. His research is currently funded by ERC, WASP, VR., and the Marie Curie doctoral training network ARMADA. His ERC project, REBOUND, focuses on understanding and mitigating phenomena of bias and polarization in online media

More info at: https://www.kth.se/profile/argioni

Title: Fairness and diversity in data summarization: theory and applications

Abstract: How can we select small but representative sets of data, search results, or news articles that are relevant but also ensure fairness and diversity criteria? In this talk we will present recent advances in algorithms for fair and diverse summarization across different domains. First, we study fairness in clustering problems, where selected representatives must proportionally reflect different groups in the data. We design methods with approximation guarantees under standard complexity assumptions. Second, we introduce sequential diversification, a new framework that captures how users consume ranked lists and we develop algorithms with provable guarantees for maximizing diversity in sequential data. Finally, we examine news aggregation, where ensuring a balanced coverage requires going beyond source diversity to capture the full range of viewpoints. Across these settings, we develop principled algorithms and validate them on real-world datasets.