معرفی
Dr. Daniel Horn is affiliated with the Department of Statistics at the Technical University of Dortmund, specifically within the Faculty of Statistics. He is part of the working group led by Prof. Dr. Andreas Groll and serves as the Study Coordinator for the B.Sc. and M.Sc. Data Science programs. His research focuses on machine learning algorithms, statistical methods for big data, and optimization techniques, with a particular emphasis on hyperparameter tuning, ensemble methods, and robust outlier detection.
Key research areas include the development of efficient algorithms for high-dimensional data analysis, such as tree ensembles for ordinal prediction, kernelized support vector machines, and model-based optimization frameworks like mlrMBO. His work also addresses industrial applications of machine learning, emphasizing practical qualification concepts for data-driven production processes.
Dr. Horn's publications span topics such as multi-objective optimization, robust outlier detection (RODD), and the Contextual Shift Method (CSM). His contributions highlight advancements in both theoretical methodologies and applied computational tools, supporting data science education and industrial innovation.
Contact: dhorn@statistik.tu-dortmund.de
