About
Andrei Caragea is a Researcher at the Mathematical Institute for Machine Learning and Data Science (MIDS) within the Faculty of Mathematics and Geography at Katholische Universität Eichstätt-Ingolstadt. His work bridges Gabor analysis, exponential bases, and neural network approximation theory, with side interests in number theory and classical geometry.
- Education: PhD in Mathematics (2023), Katholische Universität Eichstätt-Ingolstadt
- Key Collaborators: Prof. Götz Pfander, Felix Voigtlaender, Friedrich Philipp, Dae Gwan Lee
His research explores structural limitations in time-frequency representations (Balian-Low theorems for subspaces) and dimension-independent approximation capabilities of complex-valued neural networks. Recent work demonstrates how modReLU-activated networks can overcome the curse of dimensionality for Barron-class classifiers.
Publication Trends (2019-2023):
- 50% focus on Gabor systems and time-frequency invariance
- 40% on neural network expressivity with complex-valued architectures
- 10% on exponential basis constructions in signal processing
Research Grants:
- DFG Project (2019-2025): Sampling theory and bases from exponential functions
- DFG Project (2015-2022): Covariance matrix estimation under sparsity
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