Publications

List of our publications, non-exhaustive.

CVs and publications of our PIs (full list and selected 10) can be found here.
  • Rahman, S. M., Shaikh, A., Mattila, H., Lipping, T., Sari, M., Raumonen, P., & Virkki, J. (2023, November). A Hat-Integrated HCI System for Serious Games–Proof-of-Concept Applications in Focus Detection and Game Controlling. In International Conference on Games and Learning Alliance (pp. 373-382). Cham: Springer Nature Switzerland.
  • Han, T., Raumonen, P., & Sánchez-Azofeifa, G. A. (2023). A non-destructive approach to estimate buttress volume using 3D point cloud data. Ecological Informatics, 77, 102218.
  • Maity, R. R., Majumdar, A., & Nataraj, N. (2023). A priori and a posteriori error analysis for semilinear problems in liquid crystals. ESAIM: Mathematical Modelling and Numerical Analysis, 57(6), 3201-3250.
  • Burman, E., Delay, G., Ern, A., & Oksanen, L. (2023). A stability estimate for data assimilation subject to the heat equation with initial datum. Comptes Rendus. Mathématique, 361(G9), 1521-1530.
  • von Lerber, T., Lyubopytov, V. S., Ylinen, L., Lassas, M., & Küppers, F. (2023). All-optical phase memory circuit based on two coupled lasers and external optical injection. IEEE Journal of Selected Topics in Quantum Electronics, 29(2: Optical Computing), 1-11.
  • Nurminen, J. (2023). An inverse problem for the minimal surface equation. Nonlinear Analysis, 227, 113163.
  • Salminen, T., Lehtinen, K. E., & Seppänen, A. (2023). Application of the finite element method to the multicomponent general dynamic equation of aerosols. Journal of Aerosol Science, 174, 106260.
  • Xu, W., Leskinen, J., Sahlström, T., Happonen, E., Tarvainen, T., & Lehto, V. P. (2023). Assembly of fluorophore J-aggregates with nanospacer onto mesoporous nanoparticles for enhanced photoacoustic imaging. Photoacoustics, 33, 100552.
  • Koponen, E., Leskinen, J., Tarvainen, T., & Pulkkinen, A. (2023). Background-oriented schlieren sensitivity in terms of geometrical parameters of measurement setup. The Journal of the Acoustical Society of America, 154(6), 3726-3736.
  • Helin, T., Hyvönen, N., Maaninen, J., & Puska, J. P. (2023). Bayesian design of measurements for magnetorelaxometry imaging. Inverse Problems, 39(12), 125020.
  • Paternain, G. P., & Salo, M. (2018). Carleman estimates for geodesic X-ray transforms. arXiv preprint arXiv:1805.02163.
  • Niskanen, M., & Lähivaara, T. (2023). COMPOSTI: A Python-based program for seismic trans-dimensional inversion. SoftwareX, 21, 101298.
  • Jensen, B., Knudsen, K., & Schlüter, H. (2022). Conductivity reconstruction from power density data in limited view. arXiv preprint arXiv:2202.12370.
  • Sahlström, T., & Tarvainen, T. (2023, June). Deep learning in photoacoustic tomography utilizing variational autoencoders. In European Conference on Biomedical Optics (p. 1263108). Optica Publishing Group.
  • Nurminen, J. (2023). Determining an unbounded potential for an elliptic equation with a power type nonlinearity. Journal of Mathematical Analysis and Applications, 523(1), 126962.
  • Beuf, G., Hänninen, H., Lappi, T., Mäntysaari, H., & Mulian, Y. (2023). Diffractive structure function in the dipole picture. arXiv preprint arXiv:2301.09366.
  • Mozumder, M., Leskinen, J., & Tarvainen, T. (2023, June). Diffuse optical tomography setup using a nanosecond laser. In European Conference on Biomedical Optics (p. 1262820). Optica Publishing Group.
  • Herzberg, W., Hauptmann, A., & Hamilton, S. J. (2023). Domain independent post-processing with graph U-nets: applications to electrical impedance tomographic imaging⋆. Physiological Measurement, 44(12), 125008.
  • Arif, M. Z., Seppänen, A., Kolehmainen, V., & Vauhkonen, M. (2023). Dual-modal electrical imaging of two-phase flow—Experimental evaluation of the state estimation approach. Sensors, 23(9), 4462.
  • Nunes, M. H., Vaz, M. C., Camargo, J. L. C., Laurance, W. F., de Andrade, A., Vicentini, A., ... & Maeda, E. E. (2023). Edge effects on tree architecture exacerbate biomass loss of fragmented Amazonian forests. Nature Communications, 14(1), 8129.
  • Hirvi, P., Kuutela, T., Fang, Q., Hannukainen, A., Hyvönen, N., & Nissilä, I. (2023). Effects of atlas-based anatomy on modelled light transport in the neonatal head. Physics in Medicine & Biology, 68(13), 135019.
  • Maneas, E., Hauptmann, A., Alles, E. J., Xia, W., Noimark, S., David, A. L., ... & Desjardins, A. E. (2023). Enhancement of instrumented ultrasonic tracking images using deep learning. International Journal of Computer Assisted Radiology and Surgery, 18(2), 395-399.
  • Paajanen, A., Hanhela, M., Hänninen, N., Nykänen, O., Kolehmainen, V., & Nissi, M. J. (2023). Fast compressed sensing of 3D radial T1 mapping with different sparse and low-rank models. Journal of Imaging, 9(8), 151.
  • Fefferman, C., Ivanov, S., Lassas, M., & Narayanan, H. (2023). Fitting a manifold of large reach to noisy data. Journal of Topology and Analysis, 1-82.
  • Ma, S., Potenciano-Machado, L., & Salo, M. (2023). Fixed angle inverse scattering for sound speeds close to constant. SIAM Journal on Mathematical Analysis, 55(4), 3420-3456.
  • Blåsten, E., Isozaki, H., Lassas, M., & Lu, J. (2023). Gelfand’s inverse problem for the graph Laplacian. Journal of Spectral Theory, 13(1), 1-45.
  • Paternain, G. P., Salo, M., & Uhlmann, G. (2023). Geometric inverse problems (Vol. 204). Cambridge University Press.
  • Juvonen, M., Siltanen, S., & Moura, F. S. D. (2023). Helsinki deblur challenge 2021: Description of photographic data. Inverse Problems & Imaging, 17(5).
  • Hänninen, N., Pulkkinen, A., Arridge, S., & Tarvainen, T. (2023, March). Image reconstruction in quantitative photoacoustic tomography using adaptive optical Monte Carlo. In Photons Plus Ultrasound: Imaging and Sensing 2023 (Vol. 12379, pp. 246-253). SPIE.
  • Dalton, L. E., Räsänen, M., Voss, A., Seppänen, A., & Pourghaz, M. (2023). Investigating the use of electrical capacitance tomography to image rapid transient moisture flow through cracks in concrete. Cement and Concrete Composites, 140, 105070.
  • Kow, P. Z., & Wang, J. N. (2023). Landis-type conjecture for the half-Laplacian. Proceedings of the American Mathematical Society, 151(07), 2951-2962.
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  • Nicholson, R., Petra, N., Villa, U., & Kaipio, J. P. (2023). On global normal linear approximations for nonlinear Bayesian inverse problems. Inverse Problems, 39(5), 054001.
  • Kow, P. Z. (2023). On the Landis conjecture for the fractional Schrödinger equation. Journal of Spectral Theory, 12(3).
  • Pulkki, V., Daugintis, R., Lähivaara, T., & Öyry, A. (2023). Perceived difficulty of upwind shouting is a misconception explained by convective attenuation effect. Scientific Reports, 13(1), 5240.
  • Ilmavirta, J., & Kykkänen, A. (2021). Pestov identities and X-ray tomography on manifolds of low regularity. arXiv preprint arXiv:2112.05523.
  • Sahoo, S. K., & Salo, M. (2023). The linearized Calderón problem for polyharmonic operators. Journal of Differential Equations, 360, 407-451.
  • Sahlström, T., & Tarvainen, T. (2023, March). Utilizing variational autoencoders in photoacoustic tomography. In Photons Plus Ultrasound: Imaging and Sensing 2023 (Vol. 12379, pp. 233-241). SPIE.
  • Sahlström, T., & Tarvainen, T. (2023). Utilizing variational autoencoders in the Bayesian inverse problem of photoacoustic tomography. SIAM Journal on Imaging Sciences, 16(1), 89-110.
  • Mozumder , M , Hauptmann , A , Nissilä , I , Arridge , S R & Tarvainen , T 2022, ' A model-based iterative learning approach for diffuse optical tomography ' , IEEE Transactions on Medical Imaging , vol. 41 , no. 5 , pp. 1289-1299 . https://doi.org/10.1109/TMI.2021.3136461
  •  Nousiainen, J., Engler, B., Kasper, M., Helin, T., Heritier, C. T., & Rajani, C. (2022). Advances in model-based reinforcement learning for Adaptive Optics control. In L. Schreiber, D. Schmidt, & E. Vernet (Eds.), Adaptive Optics Systems VIII (Vol. 12185). (Proceedings of SPIE, the International Society for Optical Engineering). SPIE. https://doi.org/10.1117/12.2630317
  • Balehowsky, T., Kujanpää, A., Lassas, M., & Liimatainen, T. (2022). An Inverse Problem for the Relativistic Boltzmann Equation. Communications in Mathematical Physics, 396(3), 983-1049. https://doi.org/10.1007/s00220-022-04486-8
  • Suuronen, J., Chada, N.K. & Roininen, L. Cauchy Markov random field priors for Bayesian inversion. Stat Comput 32, 33 (2022). https://doi.org/10.1007/s11222-022-10089-z
  • Liimatainen , T & Oksanen , L 2022 , ' COUNTEREXAMPLES TO INVERSE PROBLEMS FOR THE WAVE EQUATION ' , Inverse problems and imaging , vol. 16 , no. 2 , pp. 467-479 . https://doi.org/10.3934/ipi.2021058
  • E. Maneas et al., "Deep Learning for Instrumented Ultrasonic Tracking: From Synthetic Training Data to In Vivo Application," in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 69, no. 2, pp. 543-552, Feb. 2022, doi: 10.1109/TUFFC.2021.3126530.
  • Chen, X., Lassas, M., Oksanen, L., & Paternain, G. P. (2022). Detection of Hermitian connections in wave equations with cubic non-linearity. European Mathematical Society. Journal, 24(7), 2191-2232. https://doi.org/10.4171/JEMS/1136
  • M. Mozumder, A. Hauptmann, S. R. Arridge, and T. Tarvainen, "Diffuse optical tomography utilizing model-based learning," in Biophotonics Congress: Biomedical Optics 2022 (Translational, Microscopy, OCT, OTS, BRAIN), Technical Digest Series (Optica Publishing Group, 2022), paper JTu3A.10.
  • Helin , T , Hyvonen , N & Puska , J P 2022 , ' Edge-Promoting Adaptive Bayesian Experimental Design for X-ray Imaging ' , SIAM Journal on Scientific Computing , vol. 44 , no. 3 , pp. B506-B530 . https://doi.org/10.1137/21M1409330
  • Hamilton SJ, Muller PA, Isaacson D, Kolehmainen V, Newell J, Rajabi Shishvan O, Saulnier G, Toivanen J. Fast absolute 3D CGO-based electrical impedance tomography on experimental tank data. Physiol Meas. 2022 Dec 6;43(12):10.1088/1361-6579/aca26b. doi: 10.1088/1361-6579/aca26b. PMID: 36374007; PMCID: PMC10028616. 
  • Hannukainen, A., Malinen, J., & Ojalammi, A. (2022). Distributed solution of Laplacian eigenvalue problems. SIAM Journal on Numerical Analysis, 60(1), 76-103. 
  • Puthawala, M., Kothari, K., Lassas, M., Dokmanić, I., & De Hoop, M. (2022). Globally injective relu networks. The Journal of Machine Learning Research, 23(1), 4544-4598. 
  • Ross, S., Arjas, A., Virtanen, I. I., Sillanpää, M. J., Roininen, L., & Hauptmann, A. (2022). Hierarchical deconvolution for incoherent scatter radar data. Atmospheric Measurement Techniques, 15(12), 3843-3857. 
  • Taskinen, H., Väisänen, A., Hatakka, L., Virtanen, T. H., Lähivaara, T., Arola, A., ... & Lipponen, A. (2022). High‐Resolution Post‐Process Corrected Satellite AOD. Geophysical Research Letters, 49(18), e2022GL099733. 
  • Lu, S., Salo, M., & Xu, B. (2022). Increasing stability in the linearized inverse Schrödinger potential problem with power type nonlinearities. Inverse Problems, 38(6), 065009.
  • Kurylev, Y., Lassas, M., Oksanen, L., & Uhlmann, G. (2014). Inverse problem for Einstein-scalar field equations. arXiv preprint arXiv:1406.4776. 
  • Liimatainen, T., Lin, Y. H., Salo, M., & Tyni, T. (2022). Inverse problems for elliptic equations with fractional power type nonlinearities. Journal of Differential Equations, 306, 189-219. 
  • Simioni, M., Arcidiacono, C., Wagner, R., Grazian, A., Gullieuszik, M., Portaluri, E., ... & Salo, A. (2022, August). LBT SOUL data as a science test bench for MICADO PSF-R tool. In Adaptive Optics Systems VIII (Vol. 12185, pp. 92-100). SPIE. 
  • Krupchyk, K., Liimatainen, T., & Salo, M. (2022). Linearized Calderón problem and exponentially accurate quasimodes for analytic manifolds. Advances in Mathematics, 403, 108362. 
  • Alexakis, S., Feizmohammadi, A., & Oksanen, L. (2022). Lorentzian Calderón problem under curvature bounds. Inventiones mathematicae, 229(1), 87-138. 
  • Gondzio, J., Lassas, M., Latva-Äijö, S. M., Siltanen, S., & Zanetti, F. (2022). Material-separating regularizer for multi-energy x-ray tomography. Inverse Problems, 38(2), 025013. 
  • Jauhiainen, J., Seppänen, A., & Valkonen, T. (2022). Mumford–Shah regularization in electrical impedance tomography with complete electrode model. Inverse Problems, 38(6), 065004. 
  • Arjas, A., Alles, E. J., Maneas, E., Arridge, S., Desjardins, A., Sillanpää, M. J., & Hauptmann, A. (2022). Neural network kalman filtering for 3-d object tracking from linear array ultrasound data. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 69(5), 1691-1702. 
  • Mathew, B., Hauptmann, A., Léon, J., & Sillanpää, M. J. (2022). NeuralLasso: Neural Networks Meet Lasso in Genomic Prediction. Frontiers in Plant Science, 13, 800161. 
  • Simioni, M., Arcidiacono, C., Wagner, R., Grazian, A., Gullieuszik, M., Portaluri, E., ... & Salo, A. (2022). Point spread function reconstruction for SOUL+ LUCI LBT data. Journal of Astronomical Telescopes, Instruments, and Systems, 8(3), 038003-038003. 
  • Kow, P. Z., Larson, S., Salo, M., & Shahgholian, H. (2022). Quadrature domains for the Helmholtz equation with applications to non-scattering phenomena. Potential Analysis, 1-38. 28. Bosi, R., Kurylev, Y., & Lassas, M. (2022). Reconstruction and stability in Gelfand’s inverse interior spectral problem. Analysis & PDE, 15(2), 273-326.
  • 42. Hamilton, S. J., Isaacson, D., Kolehmainen, V., Muller, P. A., Toivanen, J., & Bray, P. F. (2021). 3D electrical impedance tomography reconstructions from simulated electrode data using direct inversion texp and Calderón methods. Inverse problems and imaging (Springfield, Mo.), 15(5), 1135. 
  • Sofieva, V. F., Lee, H. S., Tamminen, J., Lerot, C., Romahn, F., & Loyola, D. G. (2021). A method for random uncertainties validation and probing the natural variability with application to TROPOMI on board Sentinel-5P total ozone measurements. Atmospheric Measurement Techniques, 14(4), 2993-3002. 
  • Mozumder, M., Hauptmann, A., Nissilä, I., Arridge, S. R., & Tarvainen, T. (2021). A model-based iterative learning approach for diffuse optical tomography. IEEE Transactions on Medical Imaging, 41(5), 1289-1299. 
  • Raumonen, P., Brede, B., Lau, A., & Bartholomeus, H. (2021, July). A shortest path based tree isolation method for uav lidar data. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 724-727). IEEE. 
  • Smyl, D., Tallman, T. N., Liu, D., & Hauptmann, A. (2021). An efficient quasi-Newton method for nonlinear inverse problems via learned singular values. IEEE Signal Processing Letters, 28, 748-752. 
  • He, Q., & Pursiainen, S. (2021). An extended application ‘Brain Q’processing EEG and MEG data of finger stimulation extended from ‘Zeffiro’based on machine learning and signal processing. Cognitive Systems Research, 69, 50-66. 
  • Sorsa, L. I., Pursiainen, S., & Eyraud, C. (2021). Analysis of full microwave propagation and backpropagation for a complex asteroid analogue via single-point quasi-monostatic data. Astronomy & Astrophysics, 645, A73. 
  • Salminen, T., Lehtinen, K. E., Kaipio, J. P., Russell, V., & Seppänen, A. (2022). Application of finite element method to General Dynamic Equation of Aerosols–Comparison with classical numerical approximations. Journal of Aerosol Science, 160, 105902. 
  • Candiani, V., Hyvönen, N., Kaipio, J. P., & Kolehmainen, V. (2021). Approximation error method for imaging the human head by electrical impedance tomography. Inverse Problems, 37(12), 125008. 
  • Virtanen, I. I., Tesfaw, H. W., Roininen, L., Lasanen, S., & Aikio, A. (2021). Bayesian filtering in incoherent scatter plasma parameter fits. Journal of Geophysical Research: Space Physics, 126(3), e2020JA028700. 
  • Sahlström, T., Pulkkinen, A., Leskinen, J., & Tarvainen, T. (2021, June). Computationally efficient forward model for photoacoustic tomography. In European Conference on Biomedical Optics (pp. ES1C-8). Optica Publishing Group. 
  • Hanhela, M., Gröhn, O., Kettunen, M., Niinimäki, K., Vauhkonen, M., & Kolehmainen, V. (2021). Data-driven regularization parameter selection in dynamic MRI. Journal of Imaging, 7(2), 38. 
  • Krotkov, N., Realmuto, V., Li, C., Seftor, C., Li, J., Brentzel, K., ... & Kirkendall, J. (2021). Day–Night Monitoring of Volcanic SO2 and Ash Clouds for Aviation Avoidance at Northern Polar Latitudes. Remote Sensing, 13(19), 4003. 
  • Bubba, T. A., Galinier, M., Lassas, M., Prato, M., Ratti, L., & Siltanen, S. (2021). Deep neural networks for inverse problems with pseudodifferential operators: An application to limited-angle tomography. 
  • Beyer, R. M., Basler, D., Raumonen, P., Kaasalainen, M., & Pretzsch, H. (2021). Do trees have constant branch divergence angles?. Journal of theoretical biology, 512, 110567. 
  • Schrader, S., Westhoff, A., Piastra, M. C., Miinalainen, T., Pursiainen, S., Vorwerk, J., ... & Engwer, C. (2021). DUNEuro—A software toolbox for forward modeling in bioelectromagnetism. PloS one, 16(6), e0252431. 
  • Hosseini, M., Kaasinen, A., Link, G., Lähivaara, T., & Vauhkonen, M. (2021). Electrical capacitance tomography to measure moisture distribution of polymer foam in a microwave drying process. IEEE sensors journal, 21(16), 18101-18114. 
  • Martin-Ducup, O., Mofack, G., Wang, D., Raumonen, P., Ploton, P., Sonké, B., ... & Pélissier, R. (2021). Evaluation of automated pipelines for tree and plot metric estimation from TLS data in tropical forest areas. Annals of Botany, 128(6), 753-766. 
  • Salo, M., & Shahgholian, H. (2021). Free boundary methods and non-scattering phenomena. Research in the Mathematical Sciences, 8(4), 58. 
  • Hauptmann, A., & Smyl, D. (2021). Fusing electrical and elasticity imaging. Philosophical Transactions of the Royal Society A, 379(2200), 20200194. 
  • Damialis, A., Gilles, S., Sofiev, M., Sofieva, V., Kolek, F., Bayr, D., ... & Traidl-Hoffmann, C. (2021). Higher airborne pollen concentrations correlated with increased SARS-CoV-2 infection rates, as evidenced from 31 countries across the globe. Proceedings of the National Academy of Sciences, 118(12), e2019034118. 
  • Hauptmann, A., Öktem, O., & Schönlieb, C. (2021). Image reconstruction in dynamic inverse problems with temporal models. Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging: Mathematical Imaging and Vision, 1-31. 
  • O’Sullivan, H., Raumonen, P., Kaitaniemi, P., Perttunen, J., & Sievänen, R. (2021). Integrating terrestrial laser scanning with functional–structural plant models to investigate ecological and evolutionary processes of forest communities. Annals of Botany, 128(6), 663-684. 
  • Chen, X., Lassas, M., Oksanen, L., & Paternain, G. P. (2021). Inverse problem for the Yang–Mills equations. Communications in Mathematical Physics, 384, 1187-1225. 
  • Lassas, M., Liimatainen, T., Lin, Y. H., & Salo, M. (2021). Inverse problems for elliptic equations with power type nonlinearities. Journal de mathématiques pures et appliquées, 145, 44-82. 
  • Feizmohammadi, A., Lassas, M., & Oksanen, L. (2021). Inverse problems for nonlinear hyperbolic equations with disjoint sources and receivers. In Forum of Mathematics, Pi (Vol. 9, p. e10). Cambridge University Press. 
  • Arridge, S., Fernsel, P., & Hauptmann, A. (2020). Joint reconstruction and low-rank decomposition for dynamic inverse problems. arXiv preprint arXiv:2005.14042. 
  • Arif, M. Z., Lehtikangas, O., Seppänen, A., Kolehmainen, V., & Vauhkonen, M. (2021). Joint reconstruction of conductivity and velocity in two-phase flows using electromagnetic flow tomography and electrical tomography: A simulation study. IEEE Transactions on Instrumentation and Measurement, 70, 1-17. 
  • Smyl, D., Tallman, T. N., Black, J. A., Hauptmann, A., & Liu, D. (2021). Learning and correcting non-Gaussian model errors. Journal of Computational Physics, 432, 110152. 
  • Lorente, A., Borsdorff, T., Butz, A., Hasekamp, O., Schneider, A., Wu, L., ... & Landgraf, J. (2021). Methane retrieved from TROPOMI: improvement of the data product and validation of the first 2 years of measurements. Atmospheric Measurement Techniques, 14(1), 665-684.
  • Koponen, J., Lähivaara, T., Kaipio, J., & Vauhkonen, M. (2021). Model reduction in acoustic inversion by artificial neural network. The Journal of the Acoustical Society of America, 150(5), 3435-3444. 
  • Toivanen, J., Hänninen, A., Savolainen, T., Forss, N., & Kolehmainen, V. (2021). Monitoring hemorrhagic strokes using EIT. In Bioimpedance and Spectroscopy (pp. 271-298). Academic Press. 
  • Lunz, S., Hauptmann, A., Tarvainen, T., Schonlieb, C. B., & Arridge, S. (2021). On learned operator correction in inverse problems. SIAM Journal on Imaging Sciences, 14(1), 92-127. 
  • Lassas, M., Liimatainen, T., Lin, Y. H., & Salo, M. (2020). Partial data inverse problems and simultaneous recovery of boundary and coefficients for semilinear elliptic equations. Revista Matemática Iberoamericana, 37(4), 1553-1580. 
  • Agapiou, S., Dashti, M., & Helin, T. (2021). Rates of contraction of posterior distributions based on p-exponential priors. 
  • Rezaei, A., Lahtinen, J., Neugebauer, F., Antonakakis, M., Piastra, M. C., Koulouri, A., ... & Pursiainen, S. (2021). Reconstructing subcortical and cortical somatosensory activity via the RAMUS inverse source analysis technique using median nerve SEP data. Neuroimage, 245, 118726. 
  • Brewin, R. J., Sathyendranath, S., Platt, T., Bouman, H., Ciavatta, S., Dall'Olmo, G., ... & Walker, P. (2021). Sensing the ocean biological carbon pump from space: A review of capabilities, concepts, research gaps and future developments. Earth-Science Reviews, 217, 103604.
  • Paldanius, A., Dekdouk, B., Toivanen, J., Kolehmainen, V., & Hyttinen, J. (2021). Sensitivity analysis highlights the importance of accurate head models for electrical impedance tomography monitoring of intracerebral hemorrhagic stroke. IEEE Transactions on Biomedical Engineering, 69(4), 1491-1501. 
  • Burger, M., Hauptmann, A., Helin, T., Hyvönen, N., & Puska, J. P. (2021). Sequentially optimized projections in x-ray imaging. Inverse Problems, 37(7), 075006. 
  • Agnelli, J. P., Kolehmainen, V., Lassas, M. J., Ola, P., & Siltanen, S. (2021). Simultaneous reconstruction of conductivity, boundary shape, and contact impedances in electrical impedance tomography. SIAM Journal on Imaging Sciences, 14(4), 1407-1438. 
  • Meroño, C. J., Potenciano-Machado, L., & Salo, M. (2021, November). The fixed angle scattering problem with a first-order perturbation. In Annales Henri Poincaré (Vol. 22, pp. 3699-3746). Springer International Publishing. 
  • Brede, B., Barbier, N., Bartholomeus, H., Bartolo, R., Calders, K., Derroire, G., ... & Herold, M. (2021, December). UAV-Laser Scanning based Metrics for Individual Tree Volume Estimation across Forest Types. In # PLACEHOLDER_PARENT_METADATA_VALUE# (pp. 68-70). 
  • Noël, S., Reuter, M., Buchwitz, M., Borchardt, J., Hilker, M., Bovensmann, H., ... & Warneke, T. (2021). XCO 2 retrieval for GOSAT and GOSAT-2 based on the FOCAL algorithm. Atmospheric Measurement Techniques, 14(5), 3837-3869. 

 

  • Tyni, Teemu. Numerical results for Saito's uniqueness theorem in inverse scattering theory, Inverse Problems, 2020, 35(6), 065002, DOI: 10.1088/1361-6420/ab7d2d
  • William, Rundell; Zhidong, Zhang. On the Identification of Source Term in the Heat Equation from Sparse Data. SIAM Journal Mathematical Analysis, 2020, 52(2), 1526-1548, DOI: 10.1137/19M1279915
  • Garde, Henrik; Hyvönen, Nuutti; Kuutela, Topi. On regularity of the logarithmic forward map of electrical impedance tomography, SIAM Journal on Mathematical Analysis, 2020, 52(1), 197-220, DOI: 10.1137/19M1256476
  • Garde, Henrik; Hyvönen, Nuutti. Optimal depth-dependent distinguishability bounds for electrical impedance tomography in arbitrary dimension, SIAM Journal on Applied Mathematics, 2020, 80(1), 20-43, DOI: 10.1137/19M1258761
  • Backholm, Rasmus; Bubba, Tatiana A.; Bélanger-Champagne, Camille; Helin, Tapio; Dendooven, Peter; Siltanen, Samuli. Simultaneous reconstruction of emission and attenuation in passive gamma emission tomography of spent nuclear fuel, Inverse problems and imaging, 2020, 14(2), 317-337, DOI: 10.3934/ipi.2020014
  • Shcherbacheva, Anna; Balehowsky, Tracey; Kubečka, Jakub; Olenius, Tinja; Helin, Tapio; Laine, Marko; Haario, Heikki; Kurtén, Theo; Vehkamäki, Hanna. Identification of molecular cluster evaporation rates, enthalpies and entropies by Monte Carlo method, Atmospheric Chemistry and Physics, 2020, Accepted, In Review, DOI: 10.5194/acp-2019-1036
  • Alexakis, Spyros; Balehowsky, Tracey; Nachman, Adrian. Determining a Riemannian metric from areas of minimal surfaces, Advances in Mathematics, 2020, 366(3),107025. DOI: 10.1016/j.aim.2020.107025
  • Rakesh; Salo, Mikko. The fixed angle scattering problem and wave equation inverse problems with two measurements. Inverse Problems, 2020, 36(3), 035005, DOI: 10.1088/1361-6420/ab23a2
  • Rüland, Angkana; Salo, Mikko. Quantitative approximation properties for the fractional heat equation, Mathematical Control & Related Fields, 2020, 10(1), 1-26, DOI: 10.3934/mcrf.201902
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