Hakim Qahtan is an assistant professor of Computer Science at Utrecht University and a member of the Data Intensive Systems group. His research is centered around data stream mining, data cleaning, fairness and explainability of machine learning techniques. He teaches the Data Wrangling course, which is one of the mandatory courses in the applied data science master program. Hakim also chairs the board of examiners for the artificial intelligence master program. Hakim's research is centered around data stream mining, data cleaning, fairness and explainability of machine learning techniques.

Before that, he worked as a postdoc at QCRI, where his work focused on extracting syntactic patterns that could reveal important information about the data. The discovered patterns are used to detect disguised missing values (DMVs). That is done by identifying a set of dominating patterns that generate the majority of the values in a given attribute and report the values that are generated by one of the non-dominating patterns as DMVs. The discovered syntactic patterns from the data were also used to define a new set of integrity constraints that could help in discovering data inconsistencies. The new integrity constraints are know as pattern functional dependencies (PFDs).

Hakim holds a PhD degree from the King Abdullah University of Science and Technology and an MSc. degree from King Fahd University of Petroleum and Minerals, in information and computer science, and a BSc. degree in computer science from Cairo University.