HOME         PUBLICATIONS         TEACHING


Publications and preprints

[36]
Tatiana A Bubba, Martin Burger, Tapio Helin, and Luca Ratti. Convex regularization in statistical inverse learning problems. 2021. submitted. [ bib | arXiv ]
[35]
J. Nousiainen, C. Rajani, M. Kasper, T. Helin, SY. Haffert, C. Vérinaud, J. R. Males, K. Van Gorkom, L. M. Close, J. D. Long, et al. Toward on-sky adaptive optics control using reinforcement learning-Model-based policy optimization for adaptive optics. Astronomy & Astrophysics, 664:A71, 2022. [ bib ]
[34]
Matteo Simioni, Carmelo Arcidiacono, Roland Wagner, Andrea Grazian, Marco Gullieuszik, Elisa Portaluri, Benedetta Vulcani, Anita Zanella, Guido Agapito, Richard Davies, et al. Point spread function reconstruction for SOUL+ LUCI LBT data. Journal of Astronomical Telescopes, Instruments, and Systems, 8(3):038003, 2022. [ bib ]
[33]
T. Helin and R. Kretschmann. Non-asymptotic error estimates for the Laplace approximation in Bayesian inverse problems. Numerische Mathematik, 150(2):521-549, 2022. [ bib ]
[32]
T. Helin, N. Hyvönen, and J-P. Puska. Edge-promoting adaptive Bayesian experimental design for X-ray imaging. SIAM Journal on Scientific Computing, 44(3):B506-B530, 2022. [ bib ]
[31]
J. Nousiainen, C. Rajani, M. Kasper, and T. Helin. Adaptive optics control using model-based reinforcement learning. Optics Express, 29(10):15327-15344, 2021. [ bib ]
[30]
M. Burger, A. Hauptmann, T. Helin, N. Hyvönen, and J-P. Puska. Sequentially optimized projections in x-ray imaging. Inverse Problems, 37(7):075006, 2021. [ bib | arXiv ]
[29]
S. Agapiou, M. Dashti, and T. Helin. Rates of contraction of posterior distributions based on p-exponential priors. Bernoulli, 27(3):1616-1642, 2021. [ bib | arXiv ]
[28]
F. Zolotarev, T. Eerola, L. Lensu, H. Kälviäinen, T. Helin, H. Haario, T. Kauppi, and J. Heikkinen. Modelling internal knot distribution using external log features. Computers and Electronics in Agriculture, 179:105795, 2020. [ bib ]
[27]
A. Shcherbacheva, T. Balehowsky, J. Kubečka, T. Olenius, T. Helin, H. Haario, M. Laine, T. Kurtén, and H. Vehkamäki. Identification of molecular cluster evaporation rates, cluster formation enthalpies and entropies by Monte Carlo method. Atmospheric Chemistry and Physics, 20(24):15867-15906, 2020. [ bib ]
[26]
T. Helin, M. Lassas, L. Ylinen, and Z. Zhang. Inverse problems for heat equation and space-time fractional diffusion equation with one measurement. Journal of Differential Equations, 269, 2020. [ bib | DOI | arXiv | http ]
[25]
J. Li, T. Helin, and P. Li. Inverse random source problems for time-harmonic acoustic and elastic waves. Communications in Partial Differential Equations, 45(10):1335-1380, 2020. [ bib | DOI | arXiv ]
[24]
T. Helin M. M. Dunlop and A. M. Stuart. Hyperparameter Estimation in Bayesian MAP Estimation: Parametrizations and Consistency. SMAI Journal of Computational Mathematics, 6:69-100, 2020. [ bib | arXiv | http ]
[23]
R. Virta, R. Backholm, T. A. Bubba, T. Helin, M Moring, S. Siltanen, P. Dendooven, and T. Honkamaa. Fuel rod classification from Passive Gamma Emission Tomography (PGET) of spent nuclear fuel assemblies. ESARDA Bulletin, 61:10-20, 2020. [ bib | DOI ]
[22]
R. Backholm, T. A. Bubba, C. Bélanger-Champagne, T. Helin, P. Dendooven, and S. Siltanen. Simultaneous Reconstruction of Emission and Attenuation in Passive Gamma Emission Tomography of Spent Nuclear Fuel. Inverse Problems and Imaging, 14(2), 2020. [ bib | DOI | arXiv | http ]
[21]
P. Niu, T. Helin, and Z. Zhang. An inverse random source problem in a stochastic fractional diffusion equation. Inverse Problems, 36(4):045002, 2020. [ bib | DOI | arXiv | http ]
[20]
C. Clason, T. Helin, R. Kretschmann, and P. Piiroinen. Generalised modes in Bayesian inverse problems. SIAM/ASA Journal on Uncertainty Quantification, 2(7):652-684, 2019. [ bib | arXiv ]
[19]
P. Caro, T. Helin, and M. Lassas. Inverse scattering for a random potential. Analysis and Applications, 17(04):513-567, 2019. [ bib | DOI | arXiv | http ]
[18]
P. Caro, T. Helin, A. Kujanpää, and M. Lassas. Correlation imaging in inverse scattering is tomography on probability distributions. Inverse Problems, 35(1):015010, dec 2018. [ bib | DOI | arXiv | http ]
[17]
S. Agapiou, M. Burger, M. Dashti, and T. Helin. Sparsity-promoting and edge-preserving maximum a posteriori estimators in non-parametric Bayesian inverse problems. Inverse Problems, 34(4):045002, 2018. [ bib | arXiv ]
[16]
T. Helin, S. Kindermann, J. Lehtonen, and R. Ramlau. Atmospheric turbulence profiling with an unknown power spectral density. Inverse Problems, 34(4):044002, 2018. [ bib | arXiv ]
[15]
M. Burger, T. Helin, and H. Kekkonen. Large noise in variational regularization. Transactions of Mathematics and Its Applications, 2(1), 08 2018. [ bib | DOI | arXiv | http ]
[14]
T. Helin, M. Lassas, L. Oksanen, and T. Saksala. Correlation based passive imaging with a white noise source. Journal de Mathématiques Pures et Appliquées, 116:132 - 160, 2018. [ bib | DOI | arXiv | http ]
[13]
M. Burger, H. Dirks, L. Frerking, A. Hauptmann, T. Helin, and Siltanen S. A Variational Reconstruction Method for Undersampled Dynamic X-ray Tomography based on Physical Motion Models. Inverse Problems, 33(12), 2017. [ bib | arXiv ]
[12]
T. Helin, M. Lassas, and L. Päivärinta. Inverse acoustic scattering problem in half-space with anisotropic random impedance. Journal of Differential Equations, 262(4), 2017. [ bib | arXiv ]
[11]
D. Saxenhuber, G. Auzinger, M. Le Louarn, and T. Helin. Comparison of methods for the reduction of reconstructed layers in atmospheric tomography. Applied Optics, 56(10):2621-2629, 2017. [ bib ]
[10]
T. Helin, S. Kindermann, and D. Saxenhuber. Towards analytical model optimization in atmospheric tomography. Math. Meth. in Appl. Sci., 40(4), 2016. [ bib | arXiv ]
[9]
R. Wagner, T. Helin, A. Obereder, and R. Ramlau. Efficient reconstruction method for ground layer adaptive optics with mixed natural and laser guide stars. Appl. Opt., 55(6):1421-1429, Feb 2016. [ bib ]
[8]
T. Helin and M. Burger. Maximum a posteriori probability estimates in infinite-dimensional Bayesian inverse problems. Inverse Problems, 31(8):085009, 2015. [ bib | arXiv ]
[7]
T. Helin, M. Lassas, and L. Oksanen. Inverse Problem for the Wave Equation with a White Noise Source. Comm. Math. Phys., 332(3):933-953, 2014. [ bib | arXiv ]
[6]
M. Yudytskiy, T. Helin, and R. Ramlau. A finite element-wavelet hybrid algorithm for atmospheric tomography. Journal of Optical Society of America A, 31(3):550-560, 2014. [ bib ]
[5]
T. Helin and M. Yudytskiy. Wavelet methods in multi-conjugate adaptive optics. Inverse Problems, 29(8):085003, 2013. [ bib | arXiv ]
[4]
T. Helin, M. Lassas, and L. Oksanen. An inverse problem for the wave equation with one measurement and the pseudorandom source. Anal. PDE, 5(5):887-912, 2012. [ bib | arXiv ]
[3]
T. Helin and M. Lassas. Hierarchical models in statistical inverse problems and the Mumford-Shah functional. Inverse Problems, 27(1):015008, 32, 2011. [ bib | arXiv ]
[2]
T. Helin, M. Lassas, and S. Siltanen. Infinite Photography: New Mathematical Model for High-Resolution Images. Journal of Mathematical Imaging and Vision, 36(2):140-158, 2010. [ bib ]
[1]
T. Helin. On infinite-dimensional hierarchical probability models in statistical inverse problems. Inverse Probl. Imaging, 3(4):567-597, 2009. [ bib | arXiv ]

Proceedings

[14]
Andrea Grazian, Matteo Simioni, Carmelo Arcidiacono, Jani Achren, Yann Clenet, Yixian Cao, Richard Davies, Marco Gullieuszik, Tapio Helin, Daniel Jodlbauer, et al. Status of the PSF Reconstruction Work Package for MICADO ELT. arXiv preprint arXiv:2209.03161, 2022. [ bib ]
[13]
Jalo Nousiainen, Byron Engler, Markus Kasper, Tapio Helin, Cédric T Heritier, and Chang Rajani. Advances in model-based reinforcement learning for adaptive optics control. In Adaptive Optics Systems VIII, volume 12185, pages 882-891. SPIE, 2022. [ bib ]
[12]
Byron Engler, Markus Kasper, Serban Leveratto, Cedric Taissir Heritier, Paul Bristow, Christophe Verinaud, Miska Le Louarn, Jalo Nousiainen, Tapio Helin, Markus Bonse, et al. The GPU-based High-order adaptive OpticS Testbench. In Adaptive Optics Systems VIII, volume 12185, page 1218558. SPIE, 2022. [ bib ]
[11]
Matteo Simioni, Carmelo Arcidiacono, Roland Wagner, Andrea Grazian, Marco Gullieuszik, Elisa Portaluri, Benedetta Vulcani, Anita Zanella, Guido Agapito, Richard Davies, et al. LBT SOUL data as a science test bench for MICADO PSF-R tool. In Adaptive Optics Systems VIII, volume 12185, pages 92-100. SPIE, 2022. [ bib ]
[10]
Tomi Krokberg, Jalo Nousiainen, Jonatan Lehtonen, and Tapio Helin. FitAO: a Python-based platform for algorithmic development AO. In Adaptive Optics Systems VIII, volume 12185, pages 1031-1037. SPIE, 2022. [ bib ]
[9]
Riina Virta, Rasmus Backholm, Tatiana A Bubba, Tapio Helin, Topias Kähkönen, Jaakko Leppänen, Mikael Moring, Samuli Siltanen, Peter Dendooven, and Tapani Honkamaa. Verifying spent nuclear fuel with Passive Gamma Emission Tomography prior to disposal in a geological repository in Finland. In INMM & ESARDA Joint Virtual Annual Meeting, 2021. [ bib ]
[8]
Tatiana A Bubba, Rasmus Backholm, Peter Dendooven, Tapio Helin, Samuli Siltanen, and Riina Virta. Verifying partial defects in spent nuclear fuel assemblies with Passive Gamma Emission Tomography. PROCEEDINGS OF SIMAI 2020+ 21, 2021. [ bib ]
[7]
Matteo Simioni, Carmelo Arcidiacono, Andrea Grazian, Yann Clenet, Richard Davies, Marco Gullieuszik, Gjis Verdoes Kleijn, Fernando Pedichini, Roland Wagner, Ronny Ramlau, et al. MICADO PSF-reconstruction work package description. In Adaptive Optics Systems VII, volume 11448, pages 724-733. SPIE, 2020. [ bib ]
[6]
J. Lehtonen and T. Helin. Real-time turbulence profiling using particle filtering. 2019. [ bib ]
[5]
J. Lehtonen and T. Helin. Correlation-based imaging in adaptive optics. In Mathematics in Imaging, pages MW3D-3. Optical Society of America, 2019. [ bib ]
[4]
J. Lehtonen, C. M. Correia, and T. Helin. Limits of turbulence and outer scale profiling with non-Kolmogorov statistics. In Adaptive Optics Systems VI, volume 10703, page 107036C. International Society for Optics and Photonics, 2018. [ bib ]
[3]
M. Yudytskiy, T. Helin, and R. Ramlau. A frequency dependent preconditioned wavelet method for atmospheric tomography. 2013. [ bib | DOI ]
[2]
T. Helin and M. Lassas. On the stability of MAP estimation with hierarchical prior distributions. In AIP Conference Proceedings, volume 1281, pages 1807-1810. American Institute of Physics, 2010. [ bib ]
[1]
T. Helin and M. Lassas. Bayesian signal restoration and Mumford-Shah functional. In PAMM: Proceedings in Applied Mathematics and Mechanics, volume 7, pages 2080013-2080014. Wiley Online Library, 2007. [ bib ]