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Welcome to my homepage!
After finishing my PhD at the Max-Planck-Institute for
Biological Cybernetics in Tübingen, I am now working as a research
scientist at Siemens
Corporate Technology in Munich.
My research
interests in machine learning include (but are not limited to)
non-parametric regression, regression on/onto manifolds, structured output regression,
links between kernel and Bayesian methods, dynamical systems. I have worked
in several application fields ranging from system identification,
bioinformatics, and computer graphics to medical imaging. My publications
are found below.
If you have any
questions regarding my work or if you want to collaborate in any form, do
not hesitate to contact me.
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Publications
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Journal
Articles (6)
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Steinke, F., M. Hein and B.
Schölkopf: Non-parametric Regression between
General Riemannian Manifolds, SIAM Journal on Imaging Sciences (SIIMS),
to appear, (2010)
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Steinke, F. and B. Schölkopf:
Kernels, Regularization and Differential Equations. Pattern Recognition
41(11), 3271-3286 (11 2008)
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Hofmann, M., F. Steinke, V. Scheel, G.
Charpiat, J. Farquhar, P. Aschoff, M. Brady, B. Schölkopf and B.
J. Pichler: MRI-Based Attenuation Correction for
PET/MRI: A Novel Approach Combining Pattern Recognition and Atlas
Registration. Journal of Nuclear Medicine 49(11),
1875-1883 (10 2008)
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Steinke, F., M. Hein, J. Peters
and B. Schölkopf: Manifold-valued Thin-plate
Splines with Applications in Computer Graphics. Computer
Graphics Forum 27(2), 437-448 (04 2008)
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Steinke, F., M. Seeger and K.
Tsuda: Experimental design for efficient
identification of gene regulatory networks using sparse Bayesian
models. BMC Systems Biology 1(51), 1-15 (11 2007)
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Steinke, F., B. Schölkopf and V.
Blanz: Support Vector Machines for 3D Shape
Processing. Computer Graphics Forum 24(3, EUROGRAPHICS 2005),
285-294 (09 2005)
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Conference Papers (10)
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D. Magatti, F. Steinke, M. Bundschus
and V. Tresp: Combined Structured and Keyword-Based
Search in Textually Enriched Entity-Relationship Graphs. First Workshop
on Automated Knowledge Base Construction, Grenoble (2010)
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T.A. Runkler and F. Steinke: A
New Approach to Clustering Using Eigen Decomposition. World Congress on
Computational Intelligence (WCCI), International Conference on Fuzzy Systems
(FUZZ-IEEE), Barcelona, Spain (2010)
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K.I. Kim, F. Steinke and M. Hein:
Semi-supervised Regression using Hessian energy with an application to
semi-supervised dimensionality reduction. Advances in Neural
Information Processing Systems 22: Proceedings of the 2009 Conference,
MIT Press, Cambridge, MA, USA (2009)
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Lee, D., M. Hofmann, F. Steinke, Y.
Altun, N. D. Cahill and B. Schölkopf:
Learning the Similarity Measure for Multi-Modal 3D Image Registration.
Proceedings of the IEEE Computer Society Conference on Computer Vision
and Pattern Recognition (CVPR 2009) (accepted) (06 2009)
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Steinke, F. and M. Hein:
Non-parametric Regression between Riemannian Manifolds. Advances in
Neural Information Processing Systems 21: Proceedings of the 2008
Conference, 1561-1568. (Eds.) Koller, D., D. Schuurmans, Y. Bengio, L.
Bottou, MIT Press, Cambridge, MA, USA (06 2009)
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Steinke, F., B. Schölkopf and V.
Blanz: Learning Dense 3D Correspondence.
Advances in Neural Information Processing Systems 19: Proceedings of
the 2006 Conference, 1313-1320. (Eds.) Schölkopf, B., J. Platt, T.
Hofmann, MIT Press, Cambridge, MA, USA (09 2007)
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Seeger, M., F. Steinke and K.
Tsuda: Bayesian Inference and Optimal Design
in the Sparse Linear Model. Proceedings of the 11th International
Conference on Artificial Intelligence and Statistics (AISTATS 2007),
444-451. (Eds.) Meila, M., X. Shen, Microtome, Brookline, MA, USA
(03 2007)
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Steinke, F. and B. Schölkopf: Machine
Learning Methods For Estimating Operator Equations. Proceedings of the
14th IFAC Symposium on System Identification (SYSID 2006), 1-6. (Eds.) Ninness,
B., H. Hjalmarsson, Elsevier, Oxford, United Kingdom (03 2006)
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Cooke, T., F. Steinke, C. Wallraven
and H. H. Bülthoff: A
similarity-based approach to perceptual feature validation. Proceedings
of the 2nd Symposium on Applied Perception in Graphics and
Visualization (APGV‘05), 59-66, ACM Press, New York, NY, USA (08 2005)
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Schölkopf, B., F. Steinke and V.
Blanz: Object correspondence as a machine
learning problem. Proceedings of the 22nd International Conference on
Machine Learning, 777 - 784. (Eds.) De Raedt, L., S. Wrobel (2005)
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Technical Reports (1)
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Hein, M., F. Steinke and B.
Schölkopf: Energy Functionals for
Manifold-valued Mappings and Their Properties. (167) (01
2008)
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Abstracts (1)
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Hofmann, M., F. Steinke, V. Scheel, G.
Charpiat, M. Brady, B. Schölkopf and B. J. Pichler:
MR-Based PET Attenuation Correction: Method and Validation. 2007 IEEE
Nuclear Science Symposium and Medical Imaging Conference (NSS-MIC 2007)
2007(M16-6), 1-2 (11 2007)
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Diploma & PhD Theses (2)
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Steinke, F.:
From Differential Equations to Differential Geometry: Aspects of
Regularisation in Machine Learning. (05/ 2009)
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Steinke, F.:
Implicit Surfaces For Modelling Human Heads. (09/ 2005)
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Talks (5)
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Hofmann, M., F. Steinke, P. Aschoff,
M. Lichy, M. Brady, B. Schölkopf and B. J. Pichler:
MR-Based PET Attenuation Correction: Initial Results for Whole Body.
Medical Imaging Conference, Dresden, Germany (10 2008)
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Steinke, F., M. Hein and B.
Schölkopf: Thin-Plate Splines Between Riemannian
Manifolds. Workshop on Geometry and Statistics of Shapes 2008, Bonn, Germany (06 2008)
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Hofmann, M., F. Steinke, V. Scheel, M.
Brady, B. Schölkopf and B. J. Pichler:
MR-Based PET Attenuation Correction: Method and Validation. Joint Molecular
Imaging Conference 2007, Providence, RI, USA (09 2007)
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Hofmann, M., F. Steinke, M. S.
Judenhofer, C. D. Claussen, B. Schölkopf and B. J. Pichler: A
Machine Learning Approach for Determining the PET Attenuation Map from
Magnetic Resonance Images. IEEE Medical Imaging Conference 2006,
San Diego, CA, USA (11 2006)
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Hofmann, M., F. Steinke, I. Bezrukov, A. Kolb, P. Aschoff,
M. Lichy, M. Erb, T. Nägele, M. Brady, B. Schölkopf and B. J.
Pichler: MR-based Attenuation Correction for PET/MR. (accepted),
ISMRM 2009, Honolulu, Hawaii
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