- Data Science
- Machine Learning
- Deep Learning
- +۷ مورد دیگر
Professor Stephan Doerfel holds a professorship for Data Science at Kiel University of Applied Sciences since 2021, where he is assigned to the Department of Media and serves as head of the Institute for Data Science (IfDS). He teaches Mathematics and Multivariate Statistics, Machine and Deep Learning modules in the Master's Program Data Science (MADS). His primary research interests span Data Science, Machine Learning, and Deep Learning, with significant contributions to recommender systems, social bookmarking systems (particularly BibSonomy), formal concept analysis, and publication analysis. His recent work shows a shift toward applied industrial problems, especially in injection molding quality prediction and agricultural technology. Analysis of his publication trends reveals an evolution from foundational work in formal concept analysis and social tagging systems (2010-2016) to more applied machine learning research in manufacturing and agriculture (2020-2024). His work consistently bridges theoretical computer science with practical applications across diverse domains. Professor Doerfel has been actively involved in numerous academic service roles including peer reviewing for major journals and serving on program committees for prestigious conferences including ECML/PKDD, WebSci, and ICFCA. His extensive conference participation spans over a decade with consistent contributions to the machine learning and data science community. He has supervised various research projects including AVAPS (focusing on injection molding data), PUMA (Academic Publication Management), and Info 2.0 (Informational self-determination in Web 2.0). As a core contributor to the BibSonomy open-source project, he has helped create a scholarly social bookmarking system that serves as a testbed for recommendation algorithms and user behavior studies.