| Position: | PhD |
| Room: | MF 7.070 |
| Tel (internal): | 6023 |
| Links: | Courses Presentations Projects Publications |
| External links: | TU/e page |
Recent courses
Recent presentations
Recent projects
- 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 ...
Recent publications
- An Experiment on Transfer Learning for Suffix Prediction on Event Logs - van Luijken, M., Ketykó, I., & Mannhardt, F. (2024). An Experiment on Transfer Learning for Suffix Prediction on Event Logs. In J. De Weerdt, & L. Pufahl (Eds.), Business Process Management Workshops – BPM 2023 International Workshops, Utrecht, The Netherlands, September 11–15, 2023, Revised Selected Papers (pp. 31-43). (Lecture Notes in Business Information Processing; Vol. 492 LNBIP). Springer. 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 ...