معرفی
Dr. Georg Keilbar is a Professor at the Chair of Statistics within the School of Business and Economics at Humboldt University of Berlin. His research integrates modern econometrics with machine learning, particularly focusing on structured biomedical data analysis, systemic risk modeling, and cryptocurrency dynamics. He leads the DesBi initiative, which explores the fusion of deep learning and statistical methods.
His research interests span econometric theory, panel data analysis, quantile estimation, and interdisciplinary applications in finance and biomedical fields. Recent work emphasizes nonparametric methods, causal inference, and software development for statistical challenges.
Georg’s publications (2021–2025) reflect a strong focus on bridging econometrics and machine learning, with contributions to panel data models, recursive quantile methods, and cryptocurrency dynamics. His work often addresses practical applications such as systemic risk assessment and model interpretability.
He is reachable at georg.keilbar@hu-berlin.de and located in SPA1, Room 400, Humboldt University.
