Position:	PhD
Room:	MF 7.070
Tel (internal):	6023
Links:	Courses Presentations Projects Publications
External links:	TU/e page

Recent courses

2AMI20 Advanced Process Mining - Understanding and predicting behavior of people and machines in a shared setting (task, project, factory, process, organization) is central to Data Science and Artificial Intelligence. Actions of people and machines can be recorded as discrete events in event sequences (logs), event databases (tables, graphs), and real-time event streams. Learning behavioral models of discrete event data Read More ...
JBG030 DBL Data Challenge - The objective of the Data Challenge courses is to teach students how to perform large-scale data-driven analyses themselves, combining the technical skills acquired earlier in the Data Science program with insights gained in methodological courses. In the first Data Challenge 1, students will get the possibility to apply the methods and techniques acquired during the Read More ...

Recent presentations

Smart Journey Mining: Towards successful digitalisation of services - The digitalisation of our society’s service systems has fundamentally changed the way services are delivered to, and experienced by, humans. Although digital services are supposed to simplify our lives and increase our efficiency, they often frustrate and burden customers, users, and employees. The overall goal is to increase the quality of services and support the Read More ...

Early Predicting the Need for Aftercare Based on Patients Events from the First Hours of Stay – A Case Study - Dubbeldam, A. L., Ketykó, I., de Carvalho, R. M., & Mannhardt, F. (2023). Early Predicting the Need for Aftercare Based on Patients Events from the First Hours of Stay – A Case Study. In M. Montali, A. Senderovich, & M. Weidlich (Eds.), Process Mining Workshops – ICPM 2022 International Workshops, Revised Selected Papers (pp. 366-377). (Lecture Notes in Business Information Processing; Read More ...
What Averages Do Not Tell – Predicting Real Life Processes with Sequential Deep Learning - Ketykó, I., Mannhardt, F., Hassani, M., & van Dongen, B. F. (2021). What Averages Do Not Tell – Predicting Real Life Processes with Sequential Deep Learning. CoRR, abs/2110.10225. https://arxiv.org/abs/2110.10225 Abstract Deep Learning is proven to be an effective tool for modeling sequential data as shown by the success in Natural Language, Computer Vision and Signal Read More ...