Tobias Kluth

PD Dr.- Ing. Tobias Kluth

Postdoc, Center for Industrial Mathematics, University of Bremen

Contact Me

About Me

I am a mathematician by training, I did a PhD in computer science related to neuroscience, and I finished my habilitation in mathematics/industrial mathematics. Recently, I spent one year as a deputy professor for Optimization of Complex Systems at University of Hamburg. Currently, I am affiliated at the Center for Industrial Mathematics at the University of Bremen. My main focus is on inverse problems, mathematical modelling, and parameter identification. I am fascinated by the idea to connect abstract mathematical results with various applications like, for example, imaging, magnetization dynamics of magnetic nanoparticles, image processing, aspects of neural information processing/human vision, etc. (very recently we also started looking at wind energy plants). My research interests are mainly covered by the fields of

  • inverse problems
  • deep learning for inverse problems
  • imaging modalities (current focus on magnetic particle imaging)
  • mathematical modeling and simulation
  • parameter identification in PDEs
  • algorithmic solutions, scientific computing

Short CV

Higher education

2021 Habilitation in Mathematics/Industrial Mathematics, University of Bremen.
Title: "Model-based to data-driven approaches for parameter identification and image reconstruction in the applied inverse problem of magnetic particle imaging."
2015 Dr.-Ing. in Scientific Computing/Computer Science, University of Bremen.
Title: "Intrinsic dimensionality in vision: Nonlinear filter design and applications."
Supervisor: Dr. Christoph Zetzsche.
2011 Diploma in Industrial Mathematics, University of Bremen
Title: "3D Electrical Impedance Tomography with Sparsity Constraints."

Professional appointment

04/2021-03/2022 Deputy professor, Optimization of Complex Systems, University of Hamburg
since 2016 Postdoc, ZeTeM, AG Technomathematik, University of Bremen
2011-2015 Research scientist/PhD student, Cognitive Neuroinformatics, University of Bremen

Projects

In the following you can find a selection of third-party funded projects I'm involved in:

Project KIWi "AI simulation corrections for operational lifetime extension of wind energy plants" (since 11/2022)

RTG 2224 In the near future, many wind turbines (WTs) will reach the end of their design life, typically 20 years. An extended operating life would increase the energy yield of each turbine and thus significantly reduce the energy-related greenhouse potential. An analysis of continued operation can be performed based on model-based lifetime estimation, but this requires adapting a generic WT model to enable good model performance while maintaining low complexity to enable efficient simulations. In the joint project KIWi together with Saarland university (Prof. Dr. Kathrin Flaßkamp) and Fraunhofer IWES (Dr. Tobias Meyer) funded by the BMBF, data- and model-based methods for model correction and for parameter identification in inexact models are used to enable a more accurate determination of the estimation of the load in wind turbines and thus to extend their possible lifetime. We are happy to participate in KIWi with the sub-project "Hybrid Parameter Identification with Invertible Networks", in which the connection of Deep-Operator-Networks and invertible networks for the purpose of parameter identification are investigated.

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Principal investigator in RTG 2224/2 (2nd phase since 04/2021, own project since 01/2023)

RTG 2224 In the second phase of the Research Training Group π3 on parameter identification funded by the DFG I lead a project in the 3rd cohort. It focusses on dealing with inexactly known forward operators during reconstruction. Robust reconstructions should be obtained by learning-based methods. The application of interest is magnetic particle imaging.

Find out more about the RTG

Project DELETO "Machine learning with correlative MR and high-throughput NanoCT" (since 04/2020)

RTG 2224 Machine Learning (ML) and in particular the learning of large Neural Networks (NN), the so-called Deep Learning (DL), are currently among the most viral and widely discussed scientific topics, which have applications in many research areas. In the joint project DELETO (jointly with FAU Erlangen (Prof. Dr. Martin Burger) and Saarland University (Prof. Dr. Thomas Schuster)) funded by the BMBF, the mathematical research of DL in the solution of inverse problems is to be decisively advanced in order to make the reconstruction methods based on structural priorities and motion correction in the field of correlative MR and high-throughput NanoCT, which are computationally demanding due to the large amounts of data, more accurate and efficient. The goal is to integrate these methods into next generation devices. To this end, we are working closely with corresponding industrial partners. For the first time, model-based and data-driven methods will be applied in the big data technologies of correlative magnetic resonance imaging and high-throughput NanoCT, thus leading to improved next-generation devices. We participate in DELETO with the sub-project "Invertable Residual Networks", which aims at theoretical investigations of the regularization properties of certain network architectures, invertible residual networks (IRN).

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Project D-MPI "Dynamic inverse problems in Magnetic Particle Imaging" (since 05/2020)

Magnetic particle imaging is currently in the preclinical phase. However, several crucial dynamic aspects have been left out of consideration so far to simplify modeling, data acquisition and reconstruction. In this collaborative project with Prof. Dr. Bernadette Hahn (University of Stuttgart) and me, we address three dynamic aspects of MPI resulting in a variety of dynamic inverse problems: (i) concentration dynamics, (ii) magnetic field dynamics, and (iii) particle magnetization dynamics. The project is funded by the DFG.

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Project MPI2 "Model-based parameter identification in magnetic particle imaging" (12/2016 - 05/2020)

RTG 2224 In the collaborative research project MPI2 funded by the BMBF model-based methods and their efficient realizations in algorithms are investigated since the beginning of 2017. Besides the university partners from University of Bremen (Prof. Dr. Peter Maass, Dr. Tobias Kluth), the  Aschaffenburg University of Applied Sciences (Prof. Dr. Hans-Georg Stark), Medical Center Hamburg-Eppendorf (Prof. Dr. Tobias Knopp) and  Saarland University (Prof. Dr. Thomas Schuster), industrial partners complement the consortium. SCiLS, a branch of Bruker Daltonik GmbH, and the Center for Radiology and Endoscopy of the UKE Hamburg-Eppendorf, as well as Bruker BioSpin, the manufacturer of the first commercially distributed MPI device, support the joint project.

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Postdoc project in RTG 2224/1 (10/2016 - 04/2020) - Magnetic Particle Imaging

RTG 2224 This project is embedded in the framework of the Research Training Group π3 on parameter identification funded by the DFG and allows me to investigate different aspects of parameter identification in the context of magnetic particle imaging (MPI). The problem of modeling magnetic particle imaging, with respect to finding the correct integral kernel/system function, remains an unsolved problem. Existing model-based reconstruction techniques incorporate particle behavior based on the theory of paramagnetism. Methods based on ideal magnetic fields and on realistic magnetic fields are promising but have not yet reached the necessary quality of measured system functions.

Find out more about the RTG

More on my institutional webpage

Publications

Preprints

[6]   C. Arndt, S. Dittmer, N. Heilenkötter, M. Iske, T. Kluth, J. Nickel.  Bayesian view on the training of invertible residual networks for solving linear inverse problems. Preprint, arXiv:2307.10431, 2023.

[5]   C. Arndt, A. Denker, S. Dittmer, N. Heilenkötter, M. Iske, T. Kluth, P. Maass, J. Nickel.  Invertible residual networks in the context of regularization theory for linear inverse problems. Preprint, arXiv:2306.01335, 2023.

[4]   H. Albers, T. Kluth.  Time-dependent parameter identification in a Fokker-Planck equation based magnetization model of large ensembles of nanoparticles. Preprint, arXiv:2307.03560, 2023.

[3]   C. Brandt, T. Kluth, T. Knopp, L. Westen.  Dynamic image reconstruction with motion priors in application to 3d magnetic particle imaging. Preprint, arXiv:2306.11625, 2023.

[2]   T. Lütjen, F. Schönfeld, A. Oberacker, J. Leuschner, M. Schmidt, A Wald, T. Kluth.  Learning-based approaches for reconstructions with inexact operators in nanoCTapplications. Preprint, arXiv:2307.10474, 2023.

[1]   T. Kluth, B. Jin.  Exploiting heuristic parameter choice rules for one-click image reconstruction in magnetic particle imaging. Preprint can be provided upon request, 2020.

Journal

[18]   H. Albers, T. Knopp, M. Möddel, M. Boberg, T. Kluth. Modeling the magnetization dynamics for large ensembles of immobilized magnetic nanoparticles in multi-dimensional magnetic particle imaging. In Journal of Magnetism and Magnetic Materials, 543:168534, 2022.

[17]   H. Albers, T. Kluth, T. Knopp. Simulating magnetization dynamics of large ensembles of single domain nanoparticles: Numerical study of Brown/Neel dynamics and parameter identification problems in magnetic particle imaging. In Journal of Magnetism and Magnetic Materials, 541:168508, 2022.

[16]   M. Möddel, F. Griese, T. Kluth, T. Knopp.  Estimating the spatial orientation of immobilized magnetic nanoparticles with parallel-aligned easy axes. In Physical Review Applied , 16(4):L041003, 2021.

[15]   S. Dittmer, T. Kluth, M. Henriksen, P. Maass.  Deep image prior for 3D magnetic particle imaging: A quantitative comparison of regularization techniques on Open MPI dataset. In International Journal on Magnetic Particle Imaging , 7(1):ID 2103001, 19 pages, 2021.

[14]   T. Kluth, B. Jin.  L1 data fitting for robust numerical reconstruction in magnetic particle imaging: quantitative evaluation on Open MPI dataset. In International Journal on Magnetic Particle Imaging , 6(2):ID 2012001, 23 pages, 2020.

[13]   T. Kluth, C. Bathke, M. Jiang, P. Maass.  Joint super-resolution image reconstruction and parameter identification in imaging operator: Analysis of bilinear operator equations, numerical solution, and application to magnetic particle imaging. In Inverse Problems, 36(12):124006, 2020.

[12]   S. Dittmer, T. Kluth, P. Maass, D. Otero Baguer.  Regularization by architecture: A deep prior approach for inverse problems. In Journal of Mathematical Imaging and Vision, 62(3):456-470, 2020.

[11]   T. Kluth, P. Szwargulski, T. Knopp.  Towards accurate modeling of the multidimensional magnetic particle imaging physics. In New Journal of Physics, 21(10):103032, 2019.

[10]   T. Kluth, B. Jin.  Enhanced reconstruction in magnetic particle imaging by whitening and randomized SVD approximation. In Physics in Medicine & Biology, 64(12):125026, 2019.

[9]   J. Clemens, T. Kluth, T. Reineking.  β-SLAM: Simultaneous Localization and Grid Mapping With Beta Distributions. Information Fusion, 52:62-75, 2019

[8]   T. Kluth, B. Jin, G. Li.  On the Degree of Ill-Posedness of Multi-Dimensional Magnetic Particle Imaging. In Inverse Problems, 34(9): 095006, 2018

[7]   T. Kluth.  Mathematical models for magnetic particle imaging. In Inverse Problems, 34(8): 083001, 2018

[6]   T. Kluth, P. Maass.  Model uncertainty in magnetic particle imaging: Nonlinear problem formulation and model-based sparse reconstruction. In International Journal on Magnetic Particle Imaging, Article ID 1707004, 3(2), 10 pages, 2017.

[5]   C. Bathke, T. Kluth, C. Brandt, P. Maass.  Improved image reconstruction in magnetic particle imaging using structural a priori information. In International Journal on Magnetic Particle Imaging, Article ID 1703015, 3(1), 10 pages, 2017.

[4]   J. Clemens, T. Reineking, T. Kluth. An evidential approach to SLAM, path planning, and active exploration. In International Journal of Approximate Reasoning, 73:1-26, 2016.

[3]   T. Kluth, C. Zetzsche. Numerosity as a topological invariant. In Journal of Vision, 16(3):30, 1-39, 2016.

[2]  M. Gehre, T. Kluth, C. Sebu, P. Maass. Sparse 3D reconstructions in Electrical Impedance Tomography using real data. In Inverse Problems in Science and Engineering, 22(1):31-44, 2014.

[1]  M. Gehre, T. Kluth, A. Lipponen, B. Jin, A. Seppaenen, J. P. Kaipio, P. Maass. Sparsity Reconstruction in Electrical Impedance Tomography: An Experimental Evaluation. In Journal of Computational and Applied Mathematics,, 236(8):2126-2136, 2012.

Conference articles (full paper, peer-reviewed)

[7]   S. Dittmer, T. Kluth, D. Otero Baguer, and P. Maass. A deep prior approach to magnetic particle imaging. In F. Deeba, P. Johnson, T. Würfl, and J. Ye, editors, Machine Learning for Medical Image Reconstruction , pages 113-122. Springer International Publishing, 2020.

[6]   T. Reineking, T. Kluth, D. Nakath. Adaptive information selection in images: Efficient naive bayes nearest neighbor classification. In G. Azzopardi and N. Petkov, editors, Computer Analysis of Images and Patterns, volume 9256 of Lecture Notes in Computer Science, pages 350-361. Springer International Publishing, 2015.

[5]  T. Kluth, D. Nakath, T. Reineking, C. Zetzsche, K. Schill. Affordance-based object recognition using interactions obtained from a utility maximization principle. In Computer Vision-ECCV 2014 Workshops, volume 8926 of Lecture Notes in Computer Science, pages 406-412. Springer International Publishing, 2014.

[4]  T. Kluth, C. Zetzsche. Spatial numerosity: A computational model based on a topological invariant. In C. Freksa, B. Nebel, M. Hegarty, and T. Barkowsky, editors, Spatial Cognition IX, volume 8684 of Lecture Notes in Computer Science, pages 237-252. Springer International Publishing, 2014.

[3]  D. Nakath, T. Kluth, T. Reineking, C. Zetzsche, K. Schill. Active sensorimotor object recognition in three-dimensional space. In C. Freksa, B. Nebel, M. Hegarty, and T. Barkowsky, editors, Spatial Cognition IX, volume 8684 of Lecture Notes in Computer Science, pages 312-324. Springer International Publishing, 2014.

[2]  C. Zetzsche, K. Gadzicki, T. Kluth. Statistical Invariants of Spatial Form: From Local AND to Numerosity. In Proceedings of the Second Interdisciplinary Workshop The Shape of Things, pp. 163-172, 2013.

[1]  S. Eberhardt, T. Kluth, C. Zetzsche, K. Schill. From Pattern Recognition to Place Detection. In Proceedings of the International Workshop on Place-related Knowledge Acquisition Research (P-KAR), pp. 39-44, 2012.

Conference proceedings (short contributions, peer-reviewed)

[12]  H. Albers, F. Thieben, M. Boberg, K. Scheffler, T. Knopp, T. Kluth. Model-based calibration and image reconstruction with immobilized nanoparticles. International Journal on Magnetic Particle Imaging, 9(1 Suppl. 1):ID 2303002, 5 pages, 2023.

[11]  T. Knopp, H. Albers, M. Grosser, M. Möddel, T. Kluth. Exploiting the Fourier neural operator for faster magnetization model evaluations based on the Fokker-Planck equation. International Journal on Magnetic Particle Imaging, 9(1 Suppl. 1):ID 2303003, 4 pages, 2023.

[10]   M. Möddel, A. Schlömerkemper, T. Kluth. Limitations of current MPI models in the context of fluid dynamics. International Journal on Magnetic Particle Imaging, 9(1 Suppl. 1):ID 2303078, 4 pages, 2023.

[9]   H. Albers, T. Kluth. Immobilized nanoparticles with uniaxial anisotropy in multi-dimensional lissajous-type excitation: An equilibrium model approach. International Journal on Magnetic Particle Imaging, 8(1 Suppl. 1):ID 2203048, 4 pages, 2022.

[8]   M. Nitzsche, H. Albers, T. Kluth, B. Hahn. Compensating model imperfections during image reconstruction via RESESOP. International Journal on Magnetic Particle Imaging, 8(1 Suppl. 1):ID 2203062, 4 pages, 2022.

[7]   H. Albers, T. Kluth, T. Knopp. MNPDynamics: A computational toolbox for simulating magnetic moment behavior of ensembles of nanoparticles. International Journal on Magnetic Particle Imaging, 6(2 Suppl. 1):ID 2009020, 3 pages, 2020.

[6]   T. Kluth, P. Szwargulski, T. Knopp. Towards accurate modeling of the multidimensional MPI physics. International Journal on Magnetic Particle Imaging, 6(2 Suppl. 1):ID 2009004, 3 pages, 2020.

[5]   M. Möddel, F. Griese, T. Kluth, T. Knopp. Estimating orientation using multi-contrast MPI. International Journal on Magnetic Particle Imaging, 6(2 Suppl. 1):ID 2009023, 3 pages, 2020.

[4]   T. Kluth, B. Hahn, C. Brandt. Spatio-temporal concentration reconstruction using motion priors in magnetic particle imaging. In T. Knopp and T. M. Buzug, editors, 9th International Workshop on Magnetic Particle Imaging 2019, pages 23-24. Infinite Science Publishing, 2019.

[3]   T. Kluth, B. Jin. Exploiting ill-posedness in magnetic particle imaging - system matrix approximation via randomized SVD. In T. Knopp and T. M. Buzug, editors, 8th International Workshop on Magnetic Particle Imaging 2018, pages 127-128. Infinite Science Publishing, 2018.

[2]   J. Flötotto, T. Kluth, M. Möddel, T. Knopp, P. Maass.  Improving generalization properties of measured system matrices by using regularized total least squares reconstruction in MPI. In T. Knopp and T. M. Buzug, editors, 8th International Workshop on Magnetic Particle Imaging 2018, pages 53-54. Infinite Science Publishing, 2018.

[1]   C. Bathke, T. Kluth, P. Maass. MPI reconstruction using structural prior information and sparsity. In T. Knopp and T. M. Buzug, editors, 8th International Workshop on Magnetic Particle Imaging 2018, pages 129-130. Infinite Science Publishing, 2018.

Theses

[3]  T. Kluth.  Model-based to data-driven approaches for parameter identification and image reconstruction in the applied inverse problem of magnetic particle imaging. Habilitation thesis, 2021.

[2]  T. Kluth. Intrinsic dimensionality in vision: Nonlinear filter design and applications. PhD thesis, 2015.

[1]  T. Kluth. 3D Electrical Impedance Tomography with Sparsity Constraints: Algorithm and implementation in application to the Complete Electrode Model. Diploma thesis, 2011.

Misc

[7]   T. Kluth, H. Albers.  Simulation of non-linear magnetization effects and parameter identification problems in magnetic particle imaging. Oberwolfach Report No. 39/2020, 2020.

[6]   T. Kluth.  Guest editorial: Recent developments on system function/matrix representation, hybrid simulation techniques, and magnetic actuation. In International Journal on Magnetic Particle Imaging, 6(1):ID 2010001, 3 pages, 2020.

[5]   T. Kluth and P. Maass.  Model uncertainty in magnetic particle iamging: Motivating nonlinear problems by model-based sparse reconstruction. In T. Knopp and T. M. Buzug, editors, 7th International Workshop on Magnetic Particle Imaging 2017, page 83. Infinite Science Publishing, 2017.

[4]   C. Bathke, T. Kluth, C. Brandt, P. Maass.  Improved image reconstruction in magnetic particle imaging using structural a priori information. In T. Knopp and T. M. Buzug, editors, 7th International Workshop on Magnetic Particle Imaging 2017, page 85. Infinite Science Publishing, 2017.

[3]   T. Kluth, C. Zetzsche.  Visual numerosity: A computational model based on a topological invariant. In Perception ECVP, volume 43, page 163, 2014.

[2]   T. Kluth, D. Nakath, T. Reineking, C. Zetzsche, K. Schill.  Sensorimotor integration using an information gain strategy in application to object recognition tasks. In Perception ECVP, volume 42, page 223, 2013.

[1]   S. Eberhardt, T. Kluth, M. Fahle, C. Zetzsche.  The role of nonlinearities in hierarchical feed-forward models for pattern recognition. In Perception ECVP, volume 41, page 241, 2012.

Talks and other scientific contributions

[33]  T. Kluth. Solving linear inverse problems with invertible resiudal networks. Invited talk at INdAM Workshop Learning for Inverse Problems, Rome, Italy, 2023. Slides upon request.

[32]  T. Kluth. Particle magnetization models in MPI. Invited tutorial at 12th International Workshop on Magnetic Particle Imaging, Aachen, Germany, 2023. Slides upon request.

[31]  T. Kluth. Deep Image Prior reconstruction for 3D Magnetic Particle Imaging. Invited talk at 10th International Conference on Inverse Problems: Modeling and Simulation, Malta, 2022. Slides upon request.

[30]  H. Albers, T. Kluth. Immobilized nanoparticles with uniaxial anisotropy in multi-dimensional Lissajous-type excitation: An equilibrium model approach. Talk at 11th International Workshop on Magnetic Particle Imaging, virtual conference, 2022. Slides upon request.

[29]  T. Kluth. Modeling and image reconstruction in the context of magnetic particle imaging. Invited talk at workshop Modeling, Simulation and Optimization of Fluid Dynamic Applications,, Groß Schwansee, Germany, 2021. Slides upon request.

[28]  T. Kluth. Modeling and image reconstruction in the context of magnetic particle imaging. Invited (virtual) talk at TU Braunschweig (invited by Prof. Dr. Dirk Lorenz), 2021. Slides upon request.

[27]  T. Kluth. Simulation of non-linear magnetization ef- fects and parameter identification problems in magnetic particle imaging. Invited talk at Oberwolfach workshop Computational Inverse Problems for PDEs,, virtual conference (Oberwolfach, Germany), 2020. Slides upon request.

[26]  T. Kluth. Model-based to data-driven methods for imaging in application to magnetic particle imaging. Invited talk at Imaging meets computational PDEs Workshop, virtual conference (Bath, UK), 2020. Slides upon request.

[25]  T. Kluth, P. Szwargulski, T. Knopp. Towards accurate modeling of the multidimensional MPI physics. Talk at 10th International Workshop on Magnetic Particle Imaging, virtual conference, 2020. Slides upon request.

[24]  T. Kluth. Modeling of the Magnetic Particle Imaging Physics - Theoretical insights and image reconstruction. Talk at 2nd IMA Conference On Inverse Problems From Theory To Application, London, UK, 2019. Slides upon request.

[23]  T. Kluth. Deep image priors for magnetic particle imaging. Invited talk at International Congress on Industrial and Applied Mathematics (ICIAM), Valencia, Spain, 2019. Slides upon request.

[22]  T. Kluth. Enhanced Reconstruction in Magnetic Particle Imaging by Whitening and Randomized SVD Approximation. Invited talk at 10th Applied Inverse Problems Conference, Grenoble, France, 2019. Slides upon request.

[21]   T. Kluth, B. Hahn, C. Brandt.  Spatio-temporal concentration reconstruction using motion priors in magnetic particle imaging. Talk at 9th International Workshop on Magnetic Particle Imaging, New York, USA, 2019. Slides upon request.

[20]  T. Kluth. The model-based reconstruction problem in magnetic particle imaging. Invited talk at International Conference on Sensing and Imaging, Liuzhou, China, 2018. Slides upon request.

[19]  T. Kluth. Spatio-temporal concentration estimation in magnetic particle imaging using a priori motion information. Invited talk at IFIP TC 7 Conference on System Modelling and Optimization, Essen, Germany, 2018. Slides upon request.

[18]  T. Kluth. Modeling of magnetic particle imaging. Invited talk at Universitaet Hamburg (invited by Prof. Dr. Christina Brandt), Hamburg, Germany, 2018. Slides upon request.

[17]  T. Kluth. Mathematical models in magnetic particle imaging. Talk at SIAM Conference on Imaging Science, Bologna, Italy, 2018. Slides upon request.

[16]  T. Kluth, B. Jin. Exploiting ill-posedness in magnetic particle imaging - system matrix approximation via randomized SVD. Talk at 8th International Workshop on Magnetic Particle Imaging, Hamburg, Germany, 2018. Slides upon request.

[15]  T. Kluth. Modeling of magnetic particle imaging. Invited tutorial talk at 8th International Workshop on Magnetic Particle Imaging (invited by Prof. Dr. Thorsten Buzug, Prof. Dr. Tobias Knopp), Hamburg, Germany, 2018. Slides upon request.

[14]  T. Kluth. Model-based magnetic particle imaging. Invited talk at WIAS Berlin (invited by Prof. Dr. Michael Hintermueller), Berlin, Germany, 2018. Slides upon request.

[13]  T. Kluth. Model-based magnetic particle imaging. Invited talk at Oberseminar Wissenschaftliches Rechnen, Julius-Maximilians-Universitaet Wuerzburg (invited by Prof. Dr. Bernadette Hahn), Wuerzburg, Germany, 2017. Slides upon request.

[12]  T. Kluth. Operator uncertainty in model-based magnetic particle imaging. Invited talk at Inverse Problems Seminar, University College London (invited by Dr. Bangti Jin), London, UK, 2017. Slides upon request.

[11]  T. Kluth. Nonlinear parameter identification problems for model-based magnetic particle imaging (joint work with P. Maass). Talk at 9th Applied Inverse Problems Conference, Hangzhou, China, 2017. Slides upon request.

[10]  T. Kluth, P. Maass. Model Uncertainty in Magnetic Particle Imaging. Talk at the 7th International Workshop on Magnetic Particle Imaging, Prague, 2017. Slides upon request.

[9]  C. Bathke, T. Kluth, C. Brandt, P. Maass. Magnetic Particle Imaging: Improved image reconstruction using structural a priori information. Talk at the 22nd Inverse Days, Kuopio, 2016. Slides upon request.

[8]  T. Kluth. 3D electrical impedance tomography with sparsity constraints. Invited talk at the workshop Compressive Sensing and Sparsity: Theory and Applications in Tomography, Manchester, 2015.

[7]   T. Reineking, T. Kluth, D. Nakath. Adaptive information selection in images: Efficient naive bayes nearest neighbor classification. Poster at 16th International Conference on Computer Analysis of Images and Patterns, Valletta, 2015.

[6]  T. Kluth. Intrinsic dimensionality in vision: Nonlinear filter design and applications. Talk at my Doctorate Defense, Bremen, 2015.

[5]  T. Kluth, C. Zetzsche. Spatial numerosity: A computational model based on a topological invariant. Talk at International Conference on Spatial Cognition, Bremen, 2014.

[4]  T. Kluth, C. Zetzsche. Visual numerosity: A computational model based on a topological invariant. Poster at ECVP, Belgrade, 2014.

[3]  T. Kluth, C. Zetzsche. Numerical cognition in vision based on a topological invariant. Poster at OCCAM, Osnabrueck, 2014.

[2]  T. Kluth, D. Nakath, T. Reineking, C. Zetzsche, K. Schill. Sensorimotor integration using an information gain strategy in application to object recognition tasks. Poster at ECVP, Bremen, 2013.

[1]  T. Kluth, S. Eberhardt, M. Fahle, C. Zetzsche. Slow features between invariance and selectivity. Poster at OCCAM, Osnabrueck, 2012.