Experience

  1. Data Scientist

    MdynamiX (Munich)
    • Collaborating with Bosch within context of a research project
    • Designing a reference process and tool chain
    • Building and analyzing data models
    • Developing, implementing and testing new problem targeted multi-objective optimization approaches

  2. Data Scientist

    Paretos (Heidelberg)
    • Processing and analyzing customer data in the area of dynamic pricing
    • Building ML models (e.g. Gaussian Process Regressors) for predictions
    • Delivering insights about model uncertainty

Education

  1. PhD Computational Statistics & Data Science

    LMU Munich
    My current focus is on non-stationary time series. These are time series that can show trends and shifts in distribution. Non-stationary time series are difficult to study. As a result, very few general results are known, such as central limit theorems and bootstrap methods. From a practical point of view, however, data are rarely stationary, which stresses the need for such results. The overall goal is to a) extend fundamental theorems in probability theory and statistics and b) generalise practical algorithms, such as the bootstrap, to the non-stationary context.

  2. MSc Mathematics

    University of Regensburg

    1.0 (with distinction)

    During my Master’s degree I specialised in algebraic geometry and category theory. For my Master’s thesis, I proved the proper base change theorem in etale cohomology - a crucial theorem needed to prove Weil’s Conjecture. While this theorem is well known, its proof relies on a complex series of arguments which are now - hopefully - more accessible.

    Read Thesis

  3. BSc Mathematics

    University of Regensburg

    1.4 (very good)

    During my undergraduate studies I focused on algebra and category theory. For my bachelor’s thesis, I studied locally presentable categories, which provide insights into many categorical constructions.