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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One the hottest topics in computing today is data science — how we can exploit the massive amounts of data now available to discover knowledge and solve problems. Data science touches on many computing areas (e.g., databases, machine learning, high performance computing, visualization, cloud computing, text analysis and graph analytics) and has many applications.
If you are interested in this, you might consider joining the new Mid-Maryland Data Science Meetup.
"The Mid-Maryland Data Science group is intended to be a gathering of professionals, students, and enthusiasts in the area to discuss diverse topics related to data science. We aim to have frank discussions on all topics related to the field of data analytics. No subject is too big or too small, as we believe analytics can operate on any scale. We plan to mix education with practical examples, helping users make the most of their time."
An initial meeting will be held next Tuesday (January 29) from 6:00-8:00pm at the Johns Hopkins University Applied Physics Laboratory. You can find out more about the event and reserve a spot on the meetup site. You can expect the meetings to be held about once a month in the early evening at various sites, feature one or more speakers, and provide some refreshments (e.g., pizza, soda).
These meetup events are a great way to learn about emerging technologies as well as connect with people and organizations working with them. Attending such meetings can also lead to opportunities for internships and full time jobs.
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