Research projects
The emphasis of my research projects is on the development of new methods using statistical and intelligent techniques. Although these methods are primarily developed for specific areas such as the modern manufacturing and pharmaceutical industry or health-related issues, they are also applicable in many other emerging fields in business and economics, and beyond. My research projects are therefore characterized by a broad portfolio of methodology and a high degree of interdisciplinarity with a wide range of applications.
Projects in Production / Industry
Explainable anomaly detection with lightweight federated learning
Deep learning methods for network surveillance
Monitoring of high-dimensional image data using deep learning
Security strategies for AI systems in Industry 4.0
Economic-statistical performance of AI-based monitoring tools
Profile monitoring via machine learning techniques
Effects of measurement errors on the process capability
Monitoring of periodical, finite-horizon, and high-yield processes
Sampling plans for attributes under uncertainty
Projects in Health Care / Health Economics
XAI-based models for pneumonia classification
Monitoring epidemic processes under political measures
Monitoring of healthcare processes using AI techniques
Monitoring of periodical and high-yield healthcare processesÂ
Generalized nonparametric hypothesis testing in medicine
Projects in Finance, Insurance and beyond
Prediction of bankruptcy using deep learning
Stochastic claims reserving using learning algorithms
AI techniques for book recommender systems
Statistical inference under uncertainty
Regression analysis based on AI techniques
Time series analysis based on AI techniques
Further projects in Statistics and Data Science
Innovative teaching concepts in statistics and data science
Generalized statistical distributions
Systematic reviews on AI topics
Editorials and comments