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).

Biography

  • Ph.D. Applied and Computational Mathematics specialized in numerical analysis, Royal Institute of Technology (KTH), Sweden, 2021
  • M.Sc. in Engineering, Microelectronics, International track, Japanese, Sweden, 2014

Research Interests

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

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, 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 networksIMA 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 and Computational Mathematics specialized in numerical analysis, Royal Institute of Technology (KTH), Sweden, 2021
  • M.Sc. in Engineering, Microelectronics, International track, Japanese, Sweden, 2014

Qualifications

Education

Doctor of Philosophy (Ph.D.)
Applied and Numerical Mathematics, KTH, Royal Institute of Technology, Sweden, 2021
Master of Science (M.S.)
Microelectronics Science and Engineering, KTH, Royal Institute of Engineering, Sweden, 2014

Licenses and Certifications

Languages

English
Full professional proficiency
Swedish
Native or bilingual proficiency
Finnish
Professional working proficiency
Japanese
Limited working proficiency