Current Education since 08/2012:

• PhD Candidate in Quantitative Finance and Machine Learning at the University of Basel

Completed Education:

• University of Basel: M.Sc. in Quantitative Methods

• Participation in selected courses in the fields of Accounting, Auditing, and Financial Management
• Collaboration on various projects at the Institute of Financial Management (IFF)

• Explainable Machine Learning (XML)
• Deep Learning
• Sparse Modelling
• Time Series Forecasting

• “Feature Importance for Deep Neural Networks: A Comparison of Predictive Power, Infidelity, and Sensitivity”, präsentiert an CMStatistics 2023
• "Sparse neural networks and explainability in financial statement analysis", präsentiert an COMPSTAT 2024
• Simulation Environments for illiquid Markets: Insights from Tokenized Fractional Ownership Trading and Agent-based Models, präsentiert an IFZ FinTech Conference 2024
• “Feature Importance for Deep Neural Networks: A Comparison of Predictive Power, Infidelity, and Sensitivity”, wird veröffentlicht in “Proceedings of the International Conference on Explainable AI for Neural and Symbolic Methods”

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