Thanassis Tiropanis

(Athanassios Tiropanis)

Professor of Computer Science, University of Southampton

Head, Data, Intelligence and Society (DAIS) Research Group — School of Electronics and Computer Science

Portrait of Thanassis Tiropanis

About

Thanassis Tiropanis is Professor of Computer Science at the University of Southampton, where he leads the Data, Intelligence and Society (DAIS) research group. His work builds sovereign, trustworthy data and AI infrastructures — decentralised search, privacy-preserving retrieval, and the governance of algorithmic systems — with applications spanning health, IoT and the wider digital society. Across more than 25 years he has held posts at UCL, Athens Information Technology and Southampton, including a visiting professorship at the National University of Singapore (2017–2019).

Research Interests

Selected Publications

  1. Rethinking information retrieval in a re-decentralised web: exploring the feasibility and quality of search across personal online datastores

    Bahrani, Ragab, Oliver, Tiropanis, Chapman, Poulovassilis & Roussos — 2026 — ACM Transactions on the Webdoi.org/10.1145/3777445

  2. Explanation shift: how did the distribution shift impact the model?

    Mougan, Broelemann, Kasneci, Tiropanis & Staab — 2025 — Transactions on Machine Learning Research

  3. ESPRESSO: a framework to empower search on the decentralized web

    Ragab, Savateev, Oliver, Tiropanis, Poulovassilis, Chapman & Roussos — 2024 — Data Science and Engineeringdoi.org/10.1007/s41019-024-00263-w

  4. Awakening the web of self-sovereign data with ESPRESSO: a scoping review of Solid's and Dataswyft's readiness for decentralized private search

    Oliver, Ragab, Savateev, Tiropanis, Poulovassilis, Chapman & Roussos — 2024 — doi.org/10.1049/icp.2024.2536

  5. An investigation into the feasibility of performing federated learning on social linked data servers

    Arana, Ragab & Tiropanis — 2024 — doi.org/10.1145/3589335.3651950

  6. A dual-layer privacy-preserving federated learning framework

    Huang, Tiropanis & Konstantinidis — 2023 — doi.org/10.1007/978-981-99-7254-8_19

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