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Personal profile

Personal information

Jan O. Bauer is a tenured Assistant Professor in the Department of Econometrics and Data Science at Vrije Universiteit Amsterdam and a candidate fellow at the Tinbergen Institute. He joined the department in 2023. He received his Ph.D. in Econometrics and Statistics from Universität Basel in 2023 and holds a Master’s degree in Mathematics–Economics from Københavns Universitet.

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Research

His research focuses on the analysis of cross-sectional data, typically characterized by n observations and p variables, with particular emphasis on high-dimensional settings in which p≫n.

His work lies at the intersection of statistical learning and multivariate analysis, with a focus on dimensionality reduction, variable selection, and hierarchical variable clustering. Dimensionality reduction comprises methods that represent high-dimensional data in a lower-dimensional space while preserving its essential structure. Variable selection aims to identify a subset of relevant variables that contribute most to the underlying signal or predictive task. Hierarchical variable clustering seeks to identify similarity structures among variables and organize them into meaningful clusters. 

In high-dimensional regimes (p≫n), classical statistical methods often become ill-posed or unstable, as the available observations provide insufficient information to reliably estimate model parameters. Such settings are increasingly common in modern data applications.

In addition, his research includes the development of open-source software, primarily in R, to facilitate the implementation and practical application of the proposed methods.

Teaching

Jan O. Bauer teaches, coordinates courses, and supervises theses at the Bachelor’s and Master’s levels. He is also involved in teaching in Executive Education programmes (MBA).

Ancillary activities

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User created Keywords

  • HDLSS
  • Clustering
  • Variable Clustering
  • Hierarchical Clustering
  • Statistical Learning
  • Variable Selection
  • Dimensionality Reduction
  • Principal Component Analysis
  • Singular Value Decomposition
  • Covariance Structure
  • Block Diagonality
  • Block Diagonal Covariance Structure
  • Eigendecomposition
  • Singular Vectors
  • Eigenvectors

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