Andreas Hauptmann


Assistant Professor
Computational Mathematics

27 peer reviewed publications in international journals. The publications have been cited 551 times, of which the most cited publication accounts for 134 citations. The author has a h-index of 12.00. (Google Scholar)


I am a computational mathematician interested in inverse problems and medical imaging, with expertise in tomographic reconstructions and image processing. My current work is concentrated on combining analytical and data driven methods for industrial and medical applications.

Research interests

  • Learned image reconstruction
  • Medical imaging
  • Photoacoustic tomography
  • Electrical Impedance tomography


Sawing Optimization via Deep Learning and Multi-instrument Imaging (2020-2022)
Project funded by Academy of Finland

Social media

Research groups

  • Group leader, Computational Mathematics and Inverse Problems

Selected publications

  • Lunz, Sebastian; Hauptmann, Andreas; Tarvainen, Tanja; Schönlieb, Carola-Bibiane; Arridge, Simon (2021) On Learned Operator Correction in Inverse Problems. - Siam journal on imaging sciences 14 (1), 92-127 . [Original] [Self-archived]
  • Smyl, Danny; Tallman, Tyler; Liu, Dong; Hauptmann, Andreas (2021) An efficient Quasi-Newton method for nonlinear inverse problems via learned singular values. - IEEE signal processing letters early access, early access . [Original] [Self-archived]
  • Hauptmann, Andreas; Cox, Ben (2020) Deep learning in photoacoustic tomography: current approaches and future directions. - Journal of biomedical optics 25 (11), 112903 . [Original] [Self-archived]
  • Bench, Ciaran; Hauptmann, Andreas; Cox, Ben (2020) Toward accurate quantitative photoacoustic imaging : learning vascular blood oxygen saturation in three dimensions. - Journal of biomedical optics 25 (8), 085003 . [Original] [Self-archived]
  • Arjas, A.; Roininen, L.; Sillanpää, M.J.; Hauptmann, A, (2020) Blind Hierarchical Deconvolution. (Artikkeli tieteellisessä konferenssijulkaisussa). - Proceedings of the IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP), September 21-24, 2020 Aalto University, Espoo, Finland (virtual conference). IEEE Workshop on Machine Learning for Signal Processing. 1-6. [Original] [Self-archived]
  • Arjas, Arttu; Hauptmann, Andreas; Sillanpää, Mikko J (2020) Estimation of dynamic SNP-heritability with Bayesian Gaussian process models. - Bioinformatics 36 (12), 3795-3802 . [Original] [Self-archived]
  • Arridge, S.; Hauptmann, A. (2019) Networks for Nonlinear Diffusion Problems in Imaging. - Journal of mathematical imaging and vision 62, 471–487 . [Original] [Self-archived]
  • Hamilton, S. J.; Hänninen, A.; Hauptmann, A.; Kolehmainen, V. (2019) Beltrami-net: domain-independent deep D-bar learning for absolute imaging with electrical impedance tomography (a-EIT). - Physiological measurement 40 (7), 074002 . [Original] [Self-archived]
  • Hauptmann, Andreas; Adler, Jonas; Arridge, S.R.; Oktem, Ozan (2020) Multi-Scale Learned Iterative Reconstruction. - IEEE Transactions on computational imaging early access, early access . [Original] [Self-archived]
  • Steeden, Jennifer A.; Quail, Michael; Gotschy, Alexander; Mortensen, Kristian H.; Hauptmann, Andreas; Arridge, Simon; Jones, Rodney; Muthurangu, Vivek (2020) Rapid whole-heart CMR with single volume super-resolution. - Journal of cardiovascular magnetic resonance 22 (1), 56 . [Original] [Self-archived]