Education

Throughout my PhD, I worked on several projects combining actuarial science, statistics, and machine learning. My research progressively moved from unsupervised learning and synthetic data generation towards Bayesian modelling, predictive uncertainty, and fairness in insurance pricing.

• Statistical and machine learning methods for non-life insurance.
• Bayesian modelling and uncertainty quantification.
• Fairness and responsible machine learning in insurance.
• Interpretability and actuarial consistency.
• Development of research projects from methodology to application.

The programme provided a strong mathematical and statistical foundation for actuarial work, together with applications to insurance and financial risk.

• Probability and mathematical statistics.
• Life and non-life insurance mathematics.
• Financial mathematics and stochastic processes.
• Risk modelling and actuarial valuation.
• Statistical modelling and data analysis.

My undergraduate studies gave me a broad foundation in quantitative methods and business, while also introducing me to the mathematical and statistical tools that later led me towards actuarial science.

• Mathematics, statistics, and quantitative methods.
• Economics and financial analysis.
• Management and business strategy.
• Data analysis and decision-making.
• Multidisciplinary and international coursework.

Exchange semester: Norwegian School of Economics (NHH), August 2019 – January 2020.