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Manel Slokom

Lecturer & Researcher, Hotelschool The Hague

As data and AI move into sensitive domains, I study how we can learn from data while better protecting the people behind it. My research develops and evaluates privacy-enhancing approaches, including federated learning and synthetic data, and connects them to responsible AI, fairness, and recommender systems. I apply this work across official statistics, digital media, and data governance. At the Research Centre of Hotelschool The Hague, I study personalization and customer satisfaction in HORECA, exploring how AI agents and technologies such as AR/VR can deepen our understanding of customer experiences. I hold a PhD in Computer Science from Delft University of Technology, supervised by Prof. Martha Larson. I am part of Hotelschool The Hague's Digitalization and Research group, where I teach Data Analytics and Research to first-year students and contribute to the Hospitality Research Lab for third-year students. Previously, I was a postdoctoral researcher at VU Amsterdam and CWI, and a methodologist at Statistics Netherlands (CBS). Connect with me.

Latest News

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Projects

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SynIRgy: Simulation and Synthetic Data for Information Retrieval

SynIRgy: Simulation and Synthetic Data for Information Retrieval

SynIRgy is a workshop series I co-organize bringing together researchers working on simulation and synthetic data for information retrieval. The first edition ran at ECIR 2026 in Delft; a new edition is set to join the ACM Web Conference 2027.

COACH+: Human-in-the-Loop Output Checking

COACH+: Human-in-the-Loop Output Checking

COACH+ automates statistical output checking for official statistics while keeping a human reviewer in the loop, balancing disclosure control with analyst efficiency. The work won the IAOS Prize for Young Statisticians.

Selected Publications

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  1. Methodological Choices for Clustering-based Analysis of Classification Errors
    Mana Douma, Emma Beauxis-Aussalet, Manel Slokom · BNAIC/BeNeLearn 2026, poster (accepted), 2026 Paper
  2. LLM-as-a-Discriminator: When Synthetic Tables Still Look Real
    Manel Slokom, Malek Slokom, & Thierno Kante · arXiv preprint arXiv:2606.09865, 2026 Paper Slides GitHub
  3. Beyond Centralization: User-Controlled Federated Recommendations in Practice
    Manel Slokom & Alejandro Bellogin · Proceedings of the 34th ACM Conference on User Modeling, Adaptation and Personalization, pp. 462–465, 2026 Paper Slides GitHub
  4. FedFlex: Federated Learning for Diverse Netflix Recommendations
    Sven Lankester (MS), Manel Slokom, et al. · 2nd International Conference on Federated Learning and Intelligent Computing Systems (FLICS), 2026 Slides