Philipp Grohs

Philipp Grohs is a Professor at the Faculty of Mathematics, Department of Mathematics (University of Vienna). His research spans Deep Learning, Computational Mathematics, and Applied Harmonic Analysis.

• Research Areas: Phase Retrieval, Neural Network Approximation, High-Dimensional PDEs, Manifold-Valued Data, Signal Processing
• Projects: 5 active projects (2021-2025) including deep learning for quantum chemistry and phaseless sampling

He has authored 109 peer-reviewed publications (2007-2025) with significant contributions to Gabor Phase Retrieval, Deep Neural Network Approximation, and Nonlinear Data Analysis on Manifolds. Recent work focuses on overcoming the curse of dimensionality in PDE approximations.

Scientific Prizes:

• Recipient of 2 undisclosed scientific awards (2021-2025)

Collaborations include institutions like MIT, ETH Zurich, and University of Vienna. His work appears in top venues (Nature Computational Science, Foundations of Computational Mathematics) and involves interdisciplinary applications in quantum physics and biomedical imaging.