Fabian Joachim Theis

Fabian J. Theis obtained his M.S. degree in mathematics and physics from the University of Regensburg, Germany, in 2000. He also received the Ph.D. degree in physics from the same university in 2002 and the Ph.D. degree in computer science from the University of Granada in 2003. He worked as a Visiting Researcher at the Department of Architecture and Computer Technology (University of Granada, Spain), at the RIKEN Brain Science Institute (Wako, Japan), at FAMU-FSU (Florida State University, USA), and at TUAT's Laboratory for Signal and Image Processing (Tokyo, Japan). Currently, he is heading the Signal Processing & Information Theory Group at the Institute of Biophysics at the University of Regensburg and is working on his habilitation. He serves as an Associate Editor of Computational Intelligence and Neuroscience, and is a Member of IEEE, EURASIP, and ENNS. His research interests include statistical signal processing, machine learning, blind source separation, and biomedical data analysis.

Biography Updated on 11 June 2006

Articles in Scholarly Journals [Incomplete List]

  1. Joint low-rank approximation for extracting non-Gaussian subspaces
    Signal Processing, vol. 87, no. 8, pp. 1890–1903, 2007
  2. Robust Sparse Component Analysis Based on a Generalized Hough Transform
    EURASIP Journal on Advances in Signal Processing, vol. 2007, Article ID 52105, 13 pages, 2007
  3. Median-Based Clustering for Underdetermined Blind Signal Processing
    IEEE Signal Processing Letters, vol. 13, no. 2, pp. 96–99, 2006
  4. On the Use of Simulated Annealing to Automatically Assign Decorrelated Components in Second-Order Blind Source Separation
    IEEE Transactions on Biomedical Engineering, vol. 53, no. 5, pp. 810–820, 2006
  5. On the use of sparse signal decomposition in the analysis of multi-channel surface electromyograms
    Signal Processing, vol. 86, no. 3, pp. 603–623, 2006
  6. Separation of water artifacts in 2D NOESY protein spectra using congruent matrix pencils
    Neurocomputing, vol. 69, no. 4-6, pp. 497–522, 2006
  7. Denoising using local projective subspace methods
    Neurocomputing, vol. 69, no. 13-15, pp. 1485–1501, 2006
  8. Blind source separation based on self-organizing neural network
    Engineering Applications of Artificial Intelligence, vol. 19, no. 3, pp. 305–311, 2006
  9. On model identifiability in analytic postnonlinear ICA
    Neurocomputing, vol. 64, pp. 223–234, 2005
  10. Sparse Component Analysis and Blind Source Separation of Underdetermined Mixtures
    IEEE Transactions on Neural Networks, vol. 16, no. 4, pp. 992–996, 2005
  11. A New Concept for Separability Problems in Blind Source Separation
    Neural Computation, vol. 16, no. 9, pp. 1827–1850, 2004
  12. Mobile decision support for transplantation patient data
    International Journal of Medical Informatics, vol. 73, no. 5, pp. 461–464, 2004
  13. A geometric algorithm for overcomplete linear ICA
    Neurocomputing, vol. 56, pp. 381–398, 2004
  14. Uniqueness of complex and multidimensional independent component analysis
    Signal Processing, vol. 84, no. 5, pp. 951–956, 2004
  15. Linear Geometric ICA: Fundamentals and Algorithms
    Neural Computation, vol. 15, no. 2, pp. 419–439, 2003
  16. Comparison of maximum entropy and minimal mutual information in a nonlinear setting
    Signal Processing, vol. 82, no. 7, pp. 971–980, 2002
  17. Topological Constructions in the o–Graph Calculus
    Mathematische Nachrichten, vol. 241, no. 1, pp. 170–186, 2002