Hans Weytjens ist ein Postdoc-Forscher. Seine aktuellen Forschungsinteressen umfassen präskriptive Prozessüberwachung, generativeAI, das autonome Unternehmen und RAGops.

Hans Weytjens hat einen M.Sc. in Business and Information Science von der KU Leuven, einen MBA in Finance von der University of Chicago und einen Ph.D. in Machine Learning for Predictive and Prescriptive Process Monitoring von der KU Leuven.

Forschungsinteressen

• Machine Learning
• Generative AI
• Business processes

Curriculum Vitae

Berufserfahrung

Seit 10/2023	Post-Doc	Technische Universität München
Seit 10/2023	Gast Professor	KU Leuven, Belgien
Seit 12/2000	Freelancer Strategy Consulting & Management
01/2018 - 06/2028	Freelancer Machine Learning	Unite
10/2002 - 12/2016	Eigentümer	WSW International GmbH
07/2000 - 02/2002	CFO	advanced commerce AG
1996 - 2000	Projektmanager	Roland Berger Strategy Consultants
03/1994 - 02/1996	Derivatehändler	ING AG

 

Bildung

2019 - 06/2023	PdD Kandidat	KU Leuven, Belgien
1990 - 1991	Master of Business Administration (MBA)	The University of Chicago Booth School of Business, USA
1986 - 1990		KU Leuven, Belgien

Lehre

Seit WiSe 24/25	Master Practical Course
Seit WiSe 24/25	Bachelor Practical Course

Publikationen

Wuyts, Bart, Weytjens, Hannes, vanden Broucke, Seppe, and De Weerdt, Jochen. "The DyLoPro Library: Comprehensively Profiling the Dynamics of Event Logs by Means of Visual Analytics." 2023.

Weytjens, Hannes, Verbeke, Wouter, and De Weerdt, Jochen. "Timing Process Interventions with Causal Inference and Reinforcement Learning." arXiv preprint arXiv:2306.04299, 2023.

Wuyts, Bart, Weytjens, Hannes, vanden Broucke, Seppe, and De Weerdt, Jochen. "Dylopro: Profiling the dynamics of event logs." International Conference on Business Process Management. Cham: Springer Nature Switzerland, 2023, 146–162.

Weytjens, Hannes, Verbeke, Wouter, and De Weerdt, Jochen. "Timed Process Interventions: Causal Inference vs. Reinforcement Learning." International Conference on Business Process Management. Cham: Springer Nature Switzerland, 2023, 245–258.

Weytjens, Hannes. Machine Learning for Predictive and Prescriptive Process Monitoring. 2023.

Baesens, Bart, Verbeke, Wouter, De Smedt, Johannes, De Weerdt, Jochen, and Weytjens, Hannes. Pattern and Anomaly Discovery in Data. 2023.

Baesens, Bart, Verbeke, Wouter, De Smedt, Johannes, De Weerdt, Jochen, and Weytjens, Hannes. Data Preprocessing and Feature Engineering. 2023.

Weytjens, Hannes, and De Weerdt, Jochen. "Learning uncertainty with artificial neural networks for predictive process monitoring." Applied Soft Computing 125 (2022): 109134.

Weytjens, Hannes, and De Weerdt, Jochen. "Learning uncertainty with artificial neural networks for improved remaining time prediction of business processes." International Conference on Business Process Management. Cham: Springer Nature Switzerland, 2021, 141–157.

Weytjens, Hannes, and De Weerdt, Jochen. "Creating unbiased public benchmark datasets with data leakage prevention for predictive process monitoring." International Conference on Business Process Management. Cham: Springer Nature Switzerland, 2021, 18–29.

Weytjens, Hannes, Lohmann, Enrico, and Kleinsteuber, Martin. "Cash flow prediction: MLP and LSTM compared to ARIMA and Prophet." Electronic Commerce Research 21, no. 2 (2021): 371–391.

Weytjens, Hannes, and De Weerdt, Jochen. "Process outcome prediction: CNN vs. LSTM (with attention)." Business Process Management Workshops: BPM 2020 International Workshops. Cham: Springer Nature Switzerland, 2020, 77–88