Florian Rossmannek
Assistant Professor | NTU Singapore
About Me
I am a Nanyang Assistant Professor at NTU Singapore in the Division of Mathematical Sciences doing research on the mathematics of machine learning. Prior to that, I was a Postdoctoral Fellow at NTU Singapore, mentored by Juan-Pablo Ortega. I obtained my doctoral degree from ETH Zurich in March 2023, advised by Patrick Cheridito and Arnulf Jentzen. I am an alumnus of the Schmidt AI in Science program and of the RiskLab Switzerland.
Recent Highlights
- Research visit at ETH Zurich
- New paper out with M. Petreczky, J-P. Ortega, and B. Daróczy
- Invited talk at the Asia-Pacific Summer School and Conference on Networks and Complex Systems 2026 @NTU
- Invited talk at the HUN-REN AI Symposium 2026 (picture)
- Invited talk at the 5th Symposium on Machine Learning and Dynamical Systems & Differential Equations for Data Science 2026 joint conference @Kyoto University (recording)
Previous Highlights
2025
- Invited talk at the NTU-HUN-REN SZTAKI machine learning workshop (picture)
- Research visit at the HUN-REN Institute for Computer Science and Control (SZTAKI), Budapest
- Joint workshop with NTU's EMPOWER Centre (program)
- Invited talk at the SPMS MAS Seminar Series, NTU
- Invited talk at the Differential Equations for Data Science online seminar
- Research visit at ETH Zurich
- Invited talk at the Quantitative Finance Conference 2025 @NUS (picture)
- Research visit at the University of Tokyo
- Start of the highlights section (July)
Open Positions
I am looking for Postdocs, PhD students, and Research Assistants. Inquiries can be made anytime by email. Interested applicants should have a strong mathematical background and be broadly interested in the following topics: • dynamical systems • theory of machine learning • reservoir computing • neural networks
Research Interest
I work on the mathematical foundation of machine learning, where I am particularly interested in the mechanisms of models (such as reservoir computers, state-space systems, kernel methods, etc.) used for learning and forecasting of (deterministic and stochastic) dynamical systems. My earlier work concerned approximation and optimization problems in (static) neural network theory.
Research Articles
Publications
- Stability of input-output maps and their minimal realizations in state-linear, state-affine, LPV, and linear switched systems (with M. Petreczky, J-P. Ortega, and B. Daróczy),
IEEE Control Syst. Lett., vol 10, 2026 [journal / arXiv] - Echoes of the past: A unified perspective on fading memory and echo states (with J-P. Ortega),
Neural Comp., vol 38(5), 2026 [journal / arXiv] - Stochastic dynamics learning with state-space systems (with J-P. Ortega),
Math. Models Methods Appl. Sci., vol 36(8), 2026 [journal / arXiv] - Fading memory and the convolution theorem (with J-P. Ortega)
IEEE Trans. Autom. Control, vol 70(12), 2025 [journal / arXiv] - State-space systems as dynamic generative models (with J-P. Ortega)
Proc. R. Soc. A, vol 481(2309), 2025 [journal / arXiv] - Gradient descent provably escapes saddle points in the training of shallow ReLU networks (with P. Cheridito and A. Jentzen)
J. Optim. Theory Appl., vol 203(3), 2024 [journal / arXiv] - Landscape analysis for shallow neural networks: complete classification of critical points for affine target functions (with P. Cheridito and A. Jentzen)
J. Nonlinear Sci., vol 32(5), 2022 [journal / arXiv] - A proof of convergence for gradient descent in the training of artificial neural networks for constant target functions (with P. Cheridito, A. Jentzen, and A. Riekert)
J. Complexity, vol 72, 2022 [journal / arXiv] - Non-convergence of stochastic gradient descent in the training of deep neural networks (with P. Cheridito and A. Jentzen)
J. Complexity, vol 64, 2021 [journal / arXiv] - Efficient approximation of high-dimensional functions with neural networks (with P. Cheridito and A. Jentzen)
IEEE Trans. Neural Netw. Learn. Syst., vol 33(7), 2022 [journal / arXiv]
Preprints
- Efficient Sobolev approximation of linear parabolic PDEs in high dimensions (with P. Cheridito), 2023 [arXiv]
Miscellaneous
Teaching
BSc
MH4931: Special Topics in Applied Mathematics (details)
MSc in Modelling and Simulation
MH6551: Modelling and Simulation in Data Science and Computing Engineering
Resume
Employment
| Since 2025 | Assistant Professor | NTU Singapore |
| 2023 - 2025 | Postdoctoral Fellow | NTU Singapore |
| 2019 - 2023 | Research Assistant | ETH Zurich |
Education
| 2019 - 2023 | PhD Mathematics | ETH Zurich |
| 2017 - 2019 | MSc Mathematics | ETH Zurich |
| 2014 - 2018 | BSc Mathematics | ETH Zurich |
Fellowships / Grants
| 2025 | Nanyang Assistant Professorship Startup Research Grant |
| 2023 | Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship |
Selected Talks
2026
- APCNCS (Asia-Pacific Summer School and Conference on Networks and Complex Systems); Focus Session on Complexity of AI; NTU
- HUN-REN AI Symposium; Budapest
- MLDS5 & DEDS (5th Symposium on Machine Learning and Dynamical Systems & Differential Equations for Data Science); Kyoto
2025
- NTU-HUN-REN machine learning workshop; HUN-REN SZTAKI
- Quantitative Finance Conference; Track: Advances in Quantitative Finance and Econometrics; NUS
- DEDS (Differential Equations for Data Science); Kyoto
2024
- Talks in Financial and Insurance Mathematics; ETH Zurich
- SciCADE (International Conference on Scientific Computation and Differential Equations); Minisymposium: Geometric and Multiscale Methods for High-Dimensional Dynamics; NUS
Contact
| Office MAS-05-45, NTU Singapore, 21 Nanyang Link, Singapore 637371 | |
florian.rossmannek@ntu.edu.sg | |
| Here is my official page at NTU. |