Ahmed Abdeljawad, Ph.D.
Ahmed Abdeljawad, Ph.D.
Ahmed Abdeljawad is a research scientist in the Mathematical Data Science group led by Prof. Philipp Grohs at the Johann Radon Institute for Computational and Applied Mathematics (RICAM), part of the Austrian Academy of Sciences. He is the Principal Investigator of the FWF project "Neural Networks in Infinite Dimensions" (DOI 10.55776/PAT4788625). In addition to his research activities, he teaches mathematics for artificial intelligence at Johannes Kepler University Linz.
His research focuses on the mathematical foundations of machine learning, with particular interest in approximation theory, harmonic and time-frequency analysis, and the theory of neural networks and neural operators. He develops rigorous frameworks for high- and infinite-dimensional learning and studies the expressivity, stability, and computational complexity of modern machine-learning methods, especially those arising in scientific and PDE-based applications.
DI Dr. Thomas Dittrich, B.Sc.
DI Dr. Thomas Dittrich, B.Sc.
Thomas Dittrich received his Bachelor's degree in Information Technology from the Alpen-Adria University Klagenfurt, Austria, in 2015, and his Dipl.-Ing. and Dr.techn. degrees in Electrical Engineering and Information Technology from TU Wien, Austria, in 2018 and 2023, respectively.
During his doctoral studies, he was part of the Signal Processing group under the supervision of Professor Gerald Matz. His dissertation, titled "Variational Methods for Semi-Supervised Node Classification on Signed Graphs with Multiple Classes", focused on developing and analyzing theoretical properties of non-smooth optimization methods as well as non-convex projection algorithms.
He joined the MDS group at the Austrian Academy of Sciences in April 2023, where his research focuses on approximation rates for neural networks in high-dimensional spaces and machine learning models for infinite-dimensional learning problems.
Sarah Koppensteiner, B.Sc. M.Sc.
Sarah Koppensteiner studied mathematics at the University of Viennaand completed her Masters's degree in 2017 with a thesis oncharacterizations of Gabor frames. She is interested in harmonicanalysis, in particular time-frequency analysis and anisotropicfunction spaces. She joined the research group of Prof. Philipp Grohsin 2018 as a PhD candidate. Her current research concerns anisotropicwavelets and function spaces primarily of Triebel-Lizorkin type.
Davide Nobile, B.Sc. M.Sc.
Davide Nobile, B.Sc. M.Sc.
Davide Nobile studied mathematics at the Technical University of Munich, completing his Bachelor's degree in 2021. He obtained his Master's degree in 2025 with a thesis on Fourier analysis on the group of signatures. His areas of interest include harmonic analysis, machine learning, and stochastics. Davide joined the group as a PhD candidate in 2025. His current research focuses on the analysis of numerical methods for solving the Schrödinger equation.
Panagiotis Paraschis, M.Sc. B.Sc. Ph.D.
Panagiotis Paraschis, M.Sc. B.Sc. Ph.D.
Panagiotis Paraschis earned his Bachelor's degree in Applied Mathematics in 2019 from the University of Crete and his Master's degree in Mathematical Modeling in 2021 from the National Technical University of Athens. He continued his academic path at the Department of Mathematics at the National Technical University of Athens, where he completed his doctoral studies between December 2021 and September 2025 under the supervision of Professors Konstantinos Chrysafinos and Emmanuil H. Georgoulis.
His doctoral research, titled "Space-time discretization methods for nonlinear evolutionary partial differential equations", was funded by the Hellenic Foundation for Research and Innovation (H.F.R.I) and focused on discontinuous Galerkin methods for nonlinear degenerate parabolic and elliptic PDEs, using the p-Laplacian as a model problem.
He joined the group in November 2025. His current research focuses on
1. automatic proof algorithms for inequalities in nonlinear analysis (e.g., continuous embeddings in Sobolev spaces); and
2. numerical analysis of partial differential equations.
Oliver Potocki, B.A. B.Sc. M.Sc.
Oliver Potocki, B.A. B.Sc. M.Sc.
Oliver Potocki studied business administration at the University of St.Gallen and mathematics at the Universities of Konstanz and Bonn, where he wrote his master's thesis on geometric learning of symmetric positive definite matrices. He joined the group as a PhD candidate in 2024. His current focus is on regularity properties of the Schrödinger equation and its approximability by neural networks.
DI Matthias Remta, M.Sc.
DI Matthias Remta, M.Sc.
Matthias Remta earned the Dipl.-Ing. degree in Financial and Actuarial Mathematics from TU Wien in 2022 and the MSc degree in Data Science from Technikum Wien in 2024. He worked on Reinforcement Learning for particle accelerators before joining the MDS group in 2024 as a PhD candidate. Currently, he is conducting his doctoral research on phase-space control and surrogate models for particle tracking at CERN in Switzerland.
Dr. Sebastian Schmutzhard-Höfler
Dr. Sebastian Schmutzhard-Höfler
Sebastian Schmutzhard-Höfler received the Dipl.-Ing. degree in Technical Mathematics at the University of Innsbruck in 2008, and the Dr. degree in Mathematics at the University of Vienna in 2012. After PostDoc positions at the University of Vienna, the University of Cambridge, the University of Music and Performing Arts Vienna and the Acoustics Research Institute, he joined the MDS group in October 2021, where he holds a position as a senior scientist. His research interests include numerical mathematics and efficient solutions of time dependent partial differential equations.
DI DI Dr.techn Georg Tauböck
DI DI Dr.techn Georg Tauböck
Georg Tauböck received the Dipl.-Ing. degree and the Dr.techn. degree (with distinction) in Electrical Engineering/Telecommunications and the Dipl.-Ing. degree (with distinction) in Mathematics from TU Wien, Austria, in 1999, 2005, and 2008, respectively. He also received the "Konzertfach" Diploma in Violoncello from MUK – Music and Arts University of the City of Vienna (formerly: Conservatory of Vienna), Austria, in 2000.
From 1999 to 2005, he was a junior researcher at the Telecommunications Research Center Vienna, Austria, and from 2005 to 2014, he worked as a PostDoc and project leader at the Institute of Telecommunications, TU Wien, Austria. In 2011, he was a visiting researcher at the Chair for Mathematical Information Science (formerly: Communication Technology Laboratory), ETH Zurich, Switzerland. From 2013 onwards, he held a (non-tenured) group/cluster leader position ("Mathematics and Signal Processing in Acoustics") at the Acoustics Research Institute, Austrian Academy of Sciences, Austria, and, in 2022, he returned to the Institute of Telecommunications, TU Wien, Austria.
He joined the MDS group in April 2023. His research interests include mathematics and signal processing with emphasis on compressive sensing, phase retrieval, learning, and information theory as well as on devising methods for real-word applications.
Matej Trödler, B.Sc. M.Sc.
Matej Trödler, B.Sc. M.Sc.
Matěj Trödler studied Applied Mathematical Stochastic Methods at the Czech Technical University in Prague, where he completed his bachelor's thesis in 2023. He continued in the same field at the same university and graduated in 2025 with the thesis Dispersion of a Point Set, supervised by Prof. Jan Vybíral. His interests include statistics, machine learning, data science and discrepancy/dispersion theory. He joined the MDS Group in 2025 as a PhD candidate, where he currently focuses on the approximation complexity of tanh neural networks and theory-to-practice gap problems.