About Us¶

Core Maintainers¶

Active developers and researchers who contribute to the design, implementation, and maintenance of our core libraries and tools. They oversee discussions, supervise technical decisions, and ensure consistent quality across the project.

Quentin Nater¶

Quentin Nater is a PhD student jointly supervised by Mourad Khayati and Philippe Cudré-Mauroux at the Department of Computer Science of the University of Fribourg in Switzerland. His main research interests revolve around time series analytics, with a focus on data imputation, machine learning and multimodal learning.

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Mourad Khayati¶

Mourad Khayati is a Senior Lecturer at the Department of Computer Science of the University of Fribourg, Switzerland. Prior to that he was a Senior Researcher in the eXascale Infolab group at the same university. He obtained his PhD from the University of Zurich, Switzerland, under the supervision of Prof. Michael Böhlen. His research interests lie in the field of Time Series analytics, with a special focus on data cleaning, missing values imputation, and time series data management.

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Related Papers¶

• Quentin Nater, Mourad Khayati, Jacques Pasquier: “ImputeGAP: A Comprehensive Library for Time Series Imputation” arXiv, 2025

• Mourad Khayati, Guillaume Chacun, Zakhar Tymchenko, and Philippe Cudré-Mauroux: “A-DARTS: Stable Model Selection for Data Repair in Time Series.” Proceedings of the 41st IEEE International Conference on Data Engineering (ICDE), 2025.

• Mourad Khayati, Quentin Nater, and Jacques Pasquier: “ImputeVIS: An Interactive Evaluator to Benchmark Imputation Techniques for Time Series Data.” Proceedings of the VLDB Endowment (PVLDB), 2024.

• Mourad Khayati, Ines Arous, Zakhar Tymchenko, and Philippe Cudré-Mauroux: “ORBITS: Online Recovery of Missing Blocks in Multiple Time Series Streams.” In Proceedings of the VLDB Endowment (PVLDB), 2021.

• Mourad Khayati, Alberto Lerner, Zakhar Tymchenko, and Philippe Cudré-Mauroux: “Mind the Gap: An Experimental Evaluation of Imputation of Missing Values Techniques in Time Series.” In Proceedings of the VLDB Endowment (PVLDB), 2020.

• Mourad Khayati, Philippe Cudré-Mauroux, and Michael H. Böhlen: “Scalable Recovery of Missing Blocks in Time Series with High and Low Cross-Correlations.” In the International Journal of Knowledge and Information Systems (KAIS), 2020.

• Ines Arous, Mourad Khayati, Philippe Cudré-Mauroux, Ying Zhang, Martin Kersten, and Svetlin Stalinlov: “RecovDB: Accurate and Efficient Missing Blocks Recovery for Large Time Series.” In Proceedings of the IEEE International Conference on Data Engineering (ICDE), 2019.