Education

Academic background and theoretical foundations

BSc in Econometrics and Data Science

2021 – 2024

Vrije Universiteit Amsterdam, Amsterdam, Netherlands

Completed Bachelor's degree with a focus on statistical modelling, data analysis, and econometrics. Developed strong foundations in machine learning, probability theory, data engineering, and mathematical reasoning.

  • GPA: 7.8 / 10

Bachelor Thesis — Grade: 8.5 / 10

Nonstandard Probabilities in an Internal Probability Space

Standard probability theory assigns probability zero to any individual outcome of a continuous random variable — even when that outcome is entirely possible. This thesis proposes an alternative framework using hyperreal numbers: via the ultrapower construction of ℝ* and the Loeb measure over an internal hyperfinite partition, the nonstandard probability of a specific outcome is infinitesimal but strictly positive — not zero. The thesis also establishes equivalence between the Lebesgue measure and the Loeb measure.

Supervisor: Prof. Bernd Heidergott · Co-reader: Dr. Oliver Fabert

Pure Mathematics Minor

2024

University of Leeds, Leeds, England - GPA: 8.5 / 10

Exchange semester focused on advanced pure mathematics: rigorous proofs, abstract algebra, metric spaces, and differential geometry. Courses included: Groups and Vector Spaces, Metric and Function Spaces, Calculus in the Complex Plane, Geometry of Curves and Surfaces.

Relevant Coursework

Linear AlgebraAnalysis I & IIStatisticsProbability and InferenceData Science MethodsData Structures and AlgorithmsEconometrics I, II & IIIMultivariate StatisticsNumerical MethodsLogic and Sets for CS