Machine learning
My research in machine learning focuses on applying machine learning methods, such as boosting and neural networks, to enhance predictive modeling in a way that aligns with actuarial objectives.
My research in machine learning focuses on applying machine learning methods, such as boosting and neural networks, to enhance predictive modeling in a way that aligns with actuarial objectives.
My work in insurance pricing aims to improve fairness, calibration, and predictive performance in advanced insurance pricing models.
My work in loss reserving centers on individual claim reserving, developing statistical models that improve accuracy by capturing claim-specific development patterns and heterogeneity over time.
My research on dependence is dedicated to developing statistical tools for measuring and testing dependence structures in insurance data, particularly in zero-inflated contexts.