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.
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
- (Large-Scale) Data Analytics
- Dynamic Algorithm Selection and/or Configuration
- Evolutionary Computation
- Interpretable Machine Learning / Explainable AI
(Trustworthiness of AI / ML Methods)