💯 Teaching

Lectures

• Abstraction in Machine Learning (WS25/26) (Lecturer and Organisation) (Evaluation)

Case Studies

• AutoML for Robust and Reliable Anomaly Detection (WS22/23)
• Disparities in Anomaly Detection Research [Two accepted research papers!] (WS23/24)

Seminars (as main organizer):

• Neural Networks and Deep Learning (WS21/22)
• Anomaly Detection (WS21/22)
• Uncertainty Quantification (SS22, only orga)
• Interpretable Machine Learning (SS22)
• Interpretable Machine Learning (WS22/23)
• Interpretable Machine Learning (SS23)

Seminars (as sub organizer):

• Trustworthyness, Interpretability and Fairness in Machine Learning(SS23)
• Trustworthyness, Interpretability and Fairness in Machine Learning(WS22/23)
• Trustworthyness, Interpretability and Fairness in Machine Learning(SS24)

Others:

• Big data analytics lab (SS22)
• Big data analytics lab (SS23)
• Big data analytics lab (SS24)