Berent Ånund Strømnes Lunde

Machine learning / Information theory / Computational statistics

I develop information theory for algorithms in machine learning and computational statistics. My conjecture is that, through a deeper understanding of the mathematical and statistical properties of ML-algorithms, it is possible to device smarter and more data- and information-adaptive ML-algorithms. Currently I work on theory and methods to avoid all types of manual tuning in gradient tree boosting-type methods.

• aGTBoost: Adaptive and automatic gradient boosting computations https://github.com/Blunde1/agtboost

Mathmatical finance / Actuarial mathematics

I seek more extensive usage of machine learning and advanced statistical modelling in the applied actuarial field. I believe a competetive market will require the industry to capitalize on modern statistical methodology, and see methods work in symbiosis on both risk-assessments, customer behaviour and more, to optimize value for stakeholders. To this end, the methods needs to be safe and understandable for practitioners to apply, and robustly implemented for production environments.