"Unsupervised online learning from event streams is essential for applications with limited domain knowledge and real time demands." 

Marwan Hassani is leading a small research group on streaming process mining within the Process Analytic cluster. Marwan focuses in his research on online knowledge extraction from streaming event data and its applications in stream process mining. Marwan’s main interests are developing and applying unsupervised learning methods such as clustering, outlier detection and sequential pattern mining over streaming data. His main use case is optimizing customer journey and similar applications where scalable solutions are needed in an online manner and with limited availability of domain knowledge.

Marwan Hassani has received a PhD in Computer Science in 2015 from RWTH Aachen University in Germany where he was a PhD candidate under the supervision of Thomas Seidl in the Data Management and Data Exploration Group. During his PhD, Marwan visited the School of Computing at Portsmouth University, UK. Marwan acted as a postdoctoral researcher at RWTH Aachen University from 2015 till July 2016. Since August 2016 he is an assistant professor in computer science at Eindhoven University of Technology TU/e where he is also leading the Customer Journey track of the Data Science Center Eindhoven (DSC/e). He has published over 65 scientific papers on Data Mining and Process Mining. Marwan has served in the program committees of major conferences and journals related to data mining, including ECML/PKDD, SDM, CIKM, KAIS, DAMI, JMLR. He has also co-chaired numerous scientific events on various data mining and process mining topics.