About me


No matter the reason you came across this website in the first place, stay here for a while and have a look.

What to expect from this place?

These pages contain a brief description about me (see below) and my research activities – including a list of publications, R packages that I have contributed to, and a selection of scientific events (conferences, workshops, hackathons, etc.) in whose organization I have been involved.

Who am I?

I am a data scientist working concurrently as a postdoc in the group for Data Science: Statistics and Optimization (formerly known as Information Systems and Statistics) at the University of Münster. Previously, I earned academic degrees in Data Analysis and Data Management (B.Sc.) and Data Science (M.Sc.) – both at the Department of Statistics at TU Dortmund University – and a PhD at the Department of Information Systems at the University of Münster.

Moreover, I consider myself an R-enthusiast. That is, I really enjoy to program, optimize, analyze and visualize almost anything with the statistical programming language R.

Data Science in a Nutshell

If you are now wondering what a data scientist actually is (and does), let me try to help you. Data science is a very interesting, important and recently emerging research topic, which combines technologies from computer science and statistics to (pre-)process data in a meaningful way, and afterwards extract and analyze the information contained within that data.

Research Topics

Of course, data science is of high importance and interest for several applications. However, unfortunately, I do not have the time to perform research in all of these fields, but instead decided to focus on a subset of topics that I currently find of particular interest (in alphabetical order). Those topics are basically on the intersection of optimization and machine learning:

Automated Algorithm Selection and Configuration
  • Understanding and Characterization of Problems:
    • Exploratory Landscape Analysis
      (Characterization via Scalable Features)
    • (Interactive) Problem Visualization
  • Optimization Domains:
    • Continuous (Black-Box) Optimization
    • Discrete Optimization (mainly TSP)
  • Performance Assessment and Benchmarking of Algorithms
  • Feature Selection and Algorithm Configuration
Multi-Objective Continuous Optimization
  • Visualization of Multi-Objective Landscapes
  • Development of Landscape Features
  • Landscape-Aware Algorithm Design
  • Implications for Single-Objective Optimization
  • Multi-Objective Performance Assessment
Further Topics of Interest and/or Perspective Research Avenues
  • Automated Machine Learning
  • Benchmarking
  • (Large-Scale) Data Analytics
  • Dynamic Algorithm Selection and/or Configuration
  • Evolutionary Computation
  • Interpretable Machine Learning / Explainable AI
    (Trustworthiness of AI / ML Methods)