[CSEE Talk] talk: Phlypo, Letting the data speak -- from blind to semi-blind source separation, 1pm Fri 2/1

[Tim Finin]

                          UMBC CSEE Colloquium

  Letting the data speak -- from blind to semi-blind source separation

                           Dr. Ronald Phlypo
                   Research Associate, MLSP lab, UMBC

             1:00pm Friday, 1 February 2013, ITE 227, UMBC

Blind source separation has known a vivid and rapid expansion during
the nineties. Alleviating the need for prior physical knowledge---such
as the geometry of the antenna array---allowed for data-driven
exploration of the data, based on the sole, but natural assumption of
independence. In this talk, I will focus on blind source separation,
with specific applications in biomedical signal processing. Since
independence allows to have an identifiable model under very few
assumptions on the data, it is indeed widely praised as a candidate
objective for source separation. However, it will be shown that
independence alone is not always sufficient to permit for a physically
or physiologically interpretable signal. During this talk, I will show
some proposed solutions that add minimal extra assumptions on the
data, allowing to identify physiological "sources" from
electroencephalography, electrocardiography, and functional magnetic
resonance imaging. I will also shortly demonstrate why the linear
mixture model is indeed an appropriate model for these biophysical
signals.

Ronald Phlypo obtained a degree in industrial engineering at the KHBO,
Ostend, Belgium ('03) and a master in artificial intelligence at the
KULeuven, Leuven, Belgium ('04) where he completed his master's thesis
under the supervision of prof. S. Van Huffel. While pursuing his PhD
degree at the University of Ghent, Ghent, Belgium, he visited the I3S
lab and worked with P. Comon, M. Antonini and V. Zarzoso. From Jan'10
to Feb.'12 he was a research associate at GIPSA lab, Grenoble, France
and since April'12 a research associate at the UMBC -MLSP lab,
Baltimore, USA. His research interests are in blind source separation,
statistical signal processing, and machine learning.

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