University of Fribourg | Switzerland
Publication Profile
Orcid
🌟 EARLY ACADEMIC PURSUITS
Dr. Mourad Khayati began his academic journey with a solid foundation in computer science, obtaining his MSc in 2007 from the University of Jendouba (Tunisia) and INSA-Lyon (France). His passion for data analytics led him to pursue a PhD in Computer Science from the University of Zürich (Switzerland), where he earned his doctorate in 2015 under the guidance of Prof. Michael H. Böhlen. His early research explored matrix decomposition techniques, particularly in recovering missing values in time series data, an area that would become central to his career. 🎓
🏢 PROFESSIONAL ENDEAVORS AND ACADEMIC LEADERSHIP
Currently a Senior Lecturer at the University of Fribourg in Switzerland, Dr. Khayati is involved in cutting-edge research and teaching. His expertise spans data analytics, time series repair, benchmarking, and IoT analytics. Over the years, he has not only contributed to significant advancements in these fields but also played an essential role in mentoring the next generation of computer scientists. As a co-supervisor for PhD students and a supervisor for MSc and BSc theses, his leadership in the academic community is evident. 🏫
💡 CONTRIBUTIONS AND RESEARCH FOCUS
Dr. Khayati’s research focuses on enhancing data analytics with repair functionalities, especially in handling “dirty” data. His work in the field of time series repair, IoT analytics, and benchmarking has provided novel methodologies for improving the accuracy and reliability of data. His contributions have extended to European projects such as the FashionBrain project, where he led the technical efforts to analyze Europe’s vast fashion data universe. Through the development of tools like ImputeBench and the ORBITS framework, Dr. Khayati has pushed the boundaries of data repair, helping businesses and researchers leverage incomplete data effectively. 📊
🌍 IMPACT AND INFLUENCE
Dr. Khayati’s work has had significant real-world applications. His research in missing data recovery has been applied in diverse fields, from fashion trend prediction to environmental hazard forecasting. His innovative tools have not only shaped academic research but have also influenced data-driven industries, highlighting the value of accurate, repaired datasets. Through collaborations and workshops, Dr. Khayati has spread knowledge on the importance of data integrity and the repair process across global academic and industrial forums. 🌐
🏆 ACADEMIC CITATIONS AND RECOGNITION
Dr. Khayati’s research has received widespread acclaim, evidenced by his numerous high-impact publications. His work on time series data repair has been published in prestigious journals such as VLDB, IEEE, and the Journal of Knowledge and Information Systems. With a solid record of conference presentations and awards (including the Best Experiments and Analysis Paper Award at PVLDB’20), Dr. Khayati’s contributions are frequently cited by peers, solidifying his reputation as a thought leader in the field. 🏅
💼 LEGACY AND FUTURE CONTRIBUTIONS
Looking forward, Dr. Khayati’s research continues to evolve, focusing on more efficient and scalable data repair solutions. With his ongoing supervision of PhD and MSc students and involvement in international collaborations, his legacy in the field of data science is secure. His ongoing projects promise to address new challenges, such as multimodal data repair and handling extreme-scale time series data, ensuring his continued influence on the future of data analytics. 🔮
🔑 KEY MILESTONES AND ACHIEVEMENTS
- ProvDS Project: Dr. Khayati led a project funded by the Swiss National Science Foundation (SNSF), exploring uncertain provenance management over incomplete linked data, a critical advancement for data provenance and reliability.
- FashionBrain Project: As the technical lead, Dr. Khayati made significant contributions to understanding Europe’s fashion data universe, combining research and industry application.
- Awards & Distinctions: He has received several awards for his work, including the Chair Award at ICWE’16 and the Excellence MSc Scholarship from the Tunisian Ministry of Education.
- Influence in Academic Service: Serving as a committee member for PhD defenses, program committee member for renowned conferences, and as a reviewer for top-tier journals, Dr. Khayati actively shapes the academic landscape.
Publication Top Notes
ImputeVIS: An Interactive Evaluator to Benchmark Imputation Techniques for Time Series Data
-
- Authors: Mourad Khayati, Quentin Nater, Jacques Pasquier
- Journal: Proceedings of the VLDB Endowment (Proc. VLDB Endow.)
- Year: 2024 🗓️
SEER: An End-to-End Toolkit for Benchmarking Time Series Database Systems in Monitoring Applications
-
- Authors: Luca Althaus, Mourad Khayati, Abdelouahab Khelifati, Anton Dignös, Djellel Eddine Difallah, Philippe Cudré-Mauroux
- Journal: Proceedings of the VLDB Endowment (Proc. VLDB Endow.)
- Year: 2024 🗓️
A survey of multimodal event detection based on data fusion
-
- Authors: Manuel Mondal, Philippe Cudré-Mauroux, Hông-Ân Sandlin, Philippe Cudré-Mauroux
- Journal: The VLDB Journal
- Year: 2024-12-15 🗓️
TSM-Bench: Benchmarking Time Series Database Systems for Monitoring Applications
-
- Authors: Abdelouahab Khelifati, Mourad Khayati, Anton Dignös, Djellel Difallah, Philippe Cudré-Mauroux
- Journal: Proceedings of the VLDB Endowment (Proc. VLDB Endow.)
- Year: 2023-07 🗓️
Peer Grading the Peer Reviews: A Dual-Role Approach for Lightening the Scholarly Paper Review Process
-
- Authors: Mourad Khayati
- Journal: WWW ’21: The Web Conference 2021, Virtual Event / Ljubljana, Slovenia
- Year: 2021 🗓️