A.A.A. Qahtan

Dr. A.A.A. Qahtan is an Assistant Professor in the Data Intensive Systems research group within the Department of Information and Computing Sciences at Utrecht University's Faculty of Science. His academic appointment focuses on advancing research and education in data-intensive computing with particular expertise in data stream mining, data cleaning, and explainability of machine learning techniques.

Dr. Qahtan completed his PhD studies at KAUST (King Abdullah University of Science and Technology) under the supervision of Xiangliang Zhang and Soujin Wang. Prior to joining Utrecht University, he worked as a postdoc at QCRI (Qatar Computing Research Institute) where he developed pattern functional dependencies (PFDs) for data cleaning.

His research spans several critical areas in data science:

• Data Stream Mining and Real-time Processing
• Data Cleaning and Quality Assessment
• Pattern Recognition and Functional Dependencies
• Outlier and Anomaly Detection
• Concept Drift Detection in Streaming Data
• Fairness in Machine Learning Systems
• Missing Data Imputation Techniques

Dr. Qahtan's publication record demonstrates consistent contributions to top-tier venues including PVLDB, KDD, ICDE, and SIGMOD. His recent work shows a progression from foundational data cleaning techniques to advanced applications in categorical data analysis, fairness in AI, and cryptocurrency market analysis. His research bridges theoretical foundations with practical applications across multiple domains.

Dr. Qahtan actively contributes to academic education at Utrecht University, teaching courses including Data Analytics, Data Science and Society, Data Wrangling and Data Analysis, and Databases across multiple academic years from 2019 to 2024.