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Personal profile
Quote
"Unsupervised online learning from event streams is essential for applications with limited domain knowledge and real time demands."
Research profile
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.
Academic background
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.
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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Projects
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Business Process Re-engineering and functional toolkit for GDPR compliance
van Dongen, B. F., Hassani, M., Medeiros de Carvalho, R., van Dongen, B. F., Verbeek, H. M. W., Zaman, R. & Mozafari Mehr, A.
1/05/18 → 30/04/21
Project: Research direct
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Efficient Memory Utilization in Conformance Checking of Process Event Streams
Zaman, R., Hassani, M. & van Dongen, B. F., 25 Apr 2022, p. 437-440. 4 p.Research output: Contribution to conference › Poster › Academic
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A Framework for Efficient Memory Utilization in Online Conformance Checking
Zaman, R., Hassani, M. & van Dongen, B. F., 23 Dec 2021, arXiv.org.Research output: Working paper › Academic
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Predicting Next Touch Point In A Customer Journey - A Use Case In Telecommunication
Hassani, M. & Habets, S., 1 Jun 2021, 35th ECMS INTERNATIONAL CONFERENCE ON MODELLING AND SIMULATION. 1 ed. Vol. 35. p. 48-54 7 p. (Proceedings - European Council for Modelling and Simulation, ECMS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
Open AccessFile1 Citation (Scopus)153 Downloads (Pure) -
Prefix Imputation of Orphan Events in Event Stream Processing
Zaman, R., Hassani, M. & van Dongen, B. F., 6 Oct 2021, In: Frontiers in Big Data. 4, 21 p., 705243.Research output: Contribution to journal › Article › Academic › peer-review
Open AccessFile1 Citation (Scopus)4 Downloads (Pure) -
What Averages Do Not Tell - Predicting Real Life Processes with Sequential Deep Learning
Ketykó, I., Mannhardt, F., Hassani, M. & van Dongen, B. F., 2021, In: CoRR. abs/2110.10225Research output: Contribution to journal › Article › Academic
Open Access