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Dr. Hannes Nickisch |
| Address: | Spemannstr. 38 72076 Tübingen |
| Room number: | 222 |
| Phone: | +49 7071 601 584 |
| Fax: | +49 7071 601 552 |
| E-Mail: |  hn |
My research is focussed on approximate inference and estimation in generalised linear models such as Gaussian processes and sparse linear models. Applications include MRI sequence design and image reconstruction as well as density estimation and classification.
For details, visit my homepage.
JOB
since 03/11: Philips Research Laboratories Hamburg
Research Scientist
STUDY/EDUCATION
10/10 – 03/11: Max-Planck-Institute for Biological Cybernetics
Department: Empirical Inference for Machine Learning and Perception
PostDoc
10/06 – 09/10: Max-Planck-Institute for Biological Cybernetics
Department: Empirical Inference for Machine Learning and Perception
Ph.D. student
10/04 – 09/06: Berlin University of Technology
Dual Degree of Computer Science (Maîtrise & Diplom)
Major: Artificial Intelligence, Neural Information Processing
Minor: Statistics, Pattern Recognition and Image Processing
Diploma Thesis: “Extraction of visual features from natural video data using Slow Feature Analysis”
09/03 – 06/04: Université de Nantes (France)
Maîtrise d’Informatique (1st year of Master) funded by a DAAD-scholarship
Majors: Artificial Intelligence, Language and Image Processing
10/01 – 08/03: Berlin University of Technology
Vordiplom of Computer Science
Minor: Cognitive Science
WORKING EXPERIENCE AND FURTHER EDUCATION
07/09 – 09/09: Microsoft Corporate Research, Cambridge, UK
Summer student in the Computervision Group
Topic: Interactive Segmentation
07/05 – 10/05: Siemens Corporate Research, Princeton, US
Summer student in the Imaging and Visualization Department
Evaluation and implementation of probabilistic inference on images
Topic: Nonparametric Belief Propagation
10/04 – 09/06: Berlin University of Technology
Student assistant in a project of the German Research Foundation
Neurobiologically inspired controller architecture for mobile robots
Feature extraction from video data (Optical Flow, Slow Feature Analysis)
07/04 – 09/04: Siemens Medical Solutions, Erlangen
Summer student at Magnetic Resonance/Development/Application
Implementation of an image processing algorithm on MR-T1 images
Topic: Skull Stripping (Extraction of brain matter from 3D datasets)
10/02 – 06/03: Berlin University of Technology
Student assistant in the Neural Information Processing Group
Project in the field of Computational Neuroscience:
Contrast adaptation in an orientation column in the visual cortex (V1)
Articles (6): | |
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Loktyushin A  , Nickisch H , Pohmann R and Schölkopf B (2013) Blind Retrospective Motion Correction of MR Images
Magnetic Resonance in Medicine (MRM) . in press |
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Nickisch H (2012) glm-ie: The Generalised Linear Models Inference and Estimation Toolbox
Journal of Machine Learning Research 13 1699-1703. 
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Seeger M and Nickisch H (2011) Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models
SIAM Journal on Imaging Sciences 4(1) 166-199.
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Rasmussen CE and Nickisch H (2010) Gaussian Processes for Machine Learning (GPML) Toolbox
Journal of Machine Learning Research 11 3011-3015.
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Seeger M , Nickisch H , Pohmann R and Schölkopf B (2010) Optimization of k-Space Trajectories for Compressed Sensing by Bayesian Experimental Design
Magnetic Resonance in Medicine 63(1) 116-126.
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Nickisch H and Rasmussen CE (2008) Approximations for Binary Gaussian Process Classification
Journal of Machine Learning Research 9 2035-2078.
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Conference papers (8): | |
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Seeger M and Nickisch H (2011) Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference
In: JMLR Workshop and Conference Proceedings Volume 15: AISTATS 2011, (Ed) Gordon, G. , D. Dunson, M. Dudík, 14th International Conference on Artificial Intelligence and Statistics, MIT Press, Cambridge, MA, USA, 652-660.
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, Nickisch H and Rasmussen CA (2011) Additive Gaussian Processes
In: Advances in Neural Information Processing Systems 24, (Ed) , , , and , Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS 2011), 226-234.
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Nickisch H , , and (2010) Learning an interactive segmentation system
(Ed) Chellapa, R. , P. Anandan, A. N. Rajagopalan, P. J. Narayanan, P. Torr, Seventh Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP 2010), ACM Press, Nw York, NY, USA, 274-281.
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Nickisch H and Rasmussen CE (2010) Gaussian Mixture Modeling with Gaussian Process Latent Variable Models
In: Pattern Recognition, (Ed) Goesele, M. , S. Roth, A. Kuijper, B. Schiele, K. Schindler, 32nd Annual Symposium of the German Association for Pattern Recognition (DAGM 2010), Springer, Berlin, Germany, 271-282.
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Seeger MW , Nickisch H , Pohmann R and Schölkopf B (2009) Bayesian Experimental Design of Magnetic Resonance Imaging Sequences
In: Advances in neural information processing systems 21, (Ed) , , and , 22nd Annual Conference on Neural Information Processing Systems (NIPS 2008), Curran, Red Hook, NY, USA, 1441-1448.
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Nickisch H and Seeger MW (2009) Convex variational Bayesian inference for large scale generalized linear models
In: ICML 2009, (Ed) Danyluk, A. , L. Bottou, M. Littman, 26th International Conference on Machine Learning, ACM Press, New York, NY, USA, 761-768.
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Lampert CH , Nickisch H and Harmeling S (2009) Learning To Detect Unseen Object Classes by Between-Class Attribute Transfer
In: CVPR 2009, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE Service Center, Piscataway, NJ, USA, 951-958.
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Seeger MW and Nickisch H (2008) Compressed Sensing and Bayesian Experimental Design
In: ICML 2008, (Ed) Cohen, W. W., A. McCallum, S. Roweis, 25th International Conference on Machine Learning, ACM Press, New York, NY, USA, 912-919.
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Technical reports (6): | |
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Nickisch H and : Multiple Kernel Learning: A Unifying Probabilistic Viewpoint, Max Planck Institute for Biological Cybernetics, (2011).
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Seeger M and Nickisch H : Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference, Max Planck Institute for Biological Cybernetics, (2010).
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Seeger M and Nickisch H : Large Scale Variational Inference and Experimental Design for Sparse Generalized Linear Models, Max Planck Institute for Biological Cybernetics, (2010).
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Nickisch H and Rasmussen CE : Gaussian Mixture Modeling with Gaussian Process Latent Variable Models, Max Planck Institute for Biological Cybernetics, (2010).
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Nickisch H , and : Learning an Interactive Segmentation System, Max Planck Institute for Biological Cybernetics, (2009).
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Seeger MW and Nickisch H : Large Scale Variational Inference and Experimental Design for Sparse Generalized Linear Models, 175, Max-Planck Institute for Biological Cybernetics, Tübingen, Germany, (2008).
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Posters (3): | |
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Loktyushin A , Nickisch H , Pohmann R and Schölkopf B (2012): Blind Retrospective Motion Correction of MR Images, 20th Annual Scientific Meeting ISMRM 2012, Melbourne, Australia.
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Loktyushin A , Nickisch H and Pohmann R (2011): Retrospective blind motion correction of MR images, 28th Annual Scientific Meeting ESMRMB 2011, Leipzig, Germany, Magnetic Resonance Materials in Physics, Biology and Medicine, 24(Supplement 1) 498.
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Seeger M , Nickisch H , Pohmann R and Schölkopf B (2009): Optimization of k-Space Trajectories by Bayesian Experimental Design, 17th Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM 2009), Honolulu, HI, USA.
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Theses (2): | |
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Nickisch H : Bayesian Inference and Experimental Design for Large Generalised Linear Models, Technische Universität Berlin, Berlin, Germany, (2010).
PhD thesis
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Nickisch H : Extraction of visual features from natural video data using Slow Feature Analysis, Technische Universität Berlin, Berlin, Germany, (2006).
Diplom thesis
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