Aku-Jaakko Alexis Kammonen

About

Aku-Jaakko Alexis Kammonen is a Research Scientist at Stochastic Numerics Research Group under the supervision of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST).

Research Interests

Aku's research interests include Machine Learning, Neural Networks, Random Features, Spectral Bias, Adversarial Attacks, PINNs, unsupervised learning, and Numerical Analysis.

Publications

• A. Kammonen, A. Pandey, E. von Schwerin, and R. Tempone. Adaptive random Fourier features training stabilized by resampling with applications in image regression. Foundations of Data Science, (2025).
• A. Kammonen, L. Liang, A. Pandey, and R. Tempone (2024, March 19–23). Comparing Spectral Bias and Robustness For Two-Layer Neural Networks: SGD vs Adaptive Random Fourier Features [Poster presentation], International Conference on Scientific Computing and Machine Learning 2024, Kyoto, Japan
• A. Kammonen, J. Kiessling, P. Plecháč, M. Sandberg, A. Szepessy, R. Tempone, Smaller generalization error derived for a deep residual neural network compared with shallow networks, IMA Journal of Numerical Analysis, Volume 43, Issue 5, September 2023, Pages 2585–2632
• A. Kammonen, J. Kiessling, P. Plecháč, M. Sandberg, A. Szepessy, Adaptive random Fourier features with Metropolis sampling, Foundations of Data Science, 2, 3, 309, 2020
• A. Kammonen, P. Plecháč, M. Sandberg, and A. Szepessy, (n.d.). Computational Algorithms For Canonical Ensemble Ob‑
  servables. Published as part of PhD Thesis: Numerical algorithms for high dimensional integration with application to
  machine learning and molecular dynamics (2020)
• A. Kammonen, P. Plecháč, M. Sandberg, A. Szepessy, Canonical Quantum Observables for Molecular Systems Approximated by Ab Initio Molecular Dynamics, Ann. Henri Poincaré 19, 2727–2781, 2018
• A. Kammonen, On a one-phase quasi-static Stefan problem for planar polygonal crystals grown from vapor in a bounded container, Advances in Mathematical Sciences and Applications Vol. 24, No.2 (2014), pp.317–331

Preprints

• X. Huang, A. Kammonen, A. Pandey, M. Sandberg, E. von Schwerin, A. Szepessy, and R. Tempone. Convergence for adaptive
  resampling of random Fourier features. arXiv, arXiv:2509.03151, (2025)
• O. Douglas, A. Kammonen, A. Pandey, and R. Tempone. An Adaptive Random Fourier Features approach Applied to Learning
  Stochastic Differential Equations. arXiv, arXiv:2507.15442, (2025)

Education Profile

• Ph.D., Applied Mathematics (Numerical Analysis), KTH Royal Institute of Technology, Stockholm, Sweden, 2021
• M.S., Mathematics, KTH Royal Institute of Technology, Stockholm, Sweden, 2014
• B.S., Microelectronics, KTH Royal Institute of Technology, Stockholm, Sweden, 2012