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Thursday, February 9, 2017
Unsupervised Machine Learning: Application to Data Fusion
This event is hosted/sponsored by IEEE SPS Chapter and co-sponsored by the IEEE Young Professionals.
Speaker :
Prof. Tülay Adali, Dept. of CSSE, University of Maryland Baltimore County
Location:
AMD Commons Auditorium, 991 Stewart Dr., Sunnyvale, CA (map or Google Maps)
6:00pm: Check-in
6:30pm: Announcements
6:35pm: Presentation - Part 1
7:20pm: Break - Food/Refreshments/Networking
7:50pm: Presentation - Part 2
8:45pm: Adjourn
Cost:
Free
Abstract:
Fusion of information from multiple sets of data in order to extract a set of features that are most useful and relevant
for the given task is inherent to many problems we deal with today. Data-driven methods based on source separation minimize
the assumptions about the underlying relationships and enable fusion of information by letting multiple datasets to fully
interact and inform each other. Use of multiple types of diversity - statistical property - enables maximal use of the available
information when achieving source separation. In this talk, a number of powerful models are introduced for fusion of both
multiset - data of the same nature - as well as multi-modal data, and the importance of diversity in fusion is demonstrated with a
number of practical examples in medical imaging and video processing.
Biography:
Tülay Adali received the Ph.D. degree in Electrical Engineering from North Carolina State University, Raleigh, NC, USA,
in 1992 and joined the faculty at the University of Maryland Baltimore County (UMBC), Baltimore, MD, the same year.
She is currently a Distinguished University Professor in the Department of Computer Science and Electrical Engineering at UMBC
and is the director of the Machine Learning for Signal Processing Lab (MLSP Lab).
Prof. Adali assisted in the organization of a number of international conferences and workshops including the IEEE International
Conference on Acoustics, Speech, and Signal Processing (ICASSP), the IEEE International Workshop on Neural Networks for Signal
Processing (NNSP), and the IEEE International Workshop on Machine Learning for Signal Processing (MLSP). She was the General Co-Chair,
NNSP (2001-2003); Technical Chair, MLSP (2004-2008); Program Co-Chair, MLSP (2008, 2009, and 2014), International Conference on
Independent Component Analysis and Source Separation (2009); Publicity Chair, ICASSP (2000 and 2005); and Publications Co-Chair,
ICASSP 2008. She is the Technical Program Co-Chair for ICASSP 2017 and Special Sessions Co-Chair for ICASSP 2018.
Prof. Adali chaired the IEEE Signal Processing Society (SPS) MLSP Technical Committee (2003-2005, 2011-2013), served on the SPS
Conference Board (1998-2006), IEEE SPS Signal Processing Theory and Methods (2010-2015) Technical Committee, and the IEEE SPS Bio Imaging
and Signal Processing Technical Committee (2004-2007). She was an Associate Editor for IEEE Transactions on Signal Processing (2003-2006),
IEEE Transactions on Biomedical Engineering (2007-2013), IEEE Journal of Selected Areas in Signal Processing (2010-2013), and Elsevier Signal
Processing Journal (2007-2010). She is currently serving on the Editorial Boards of the Proceedings of the IEEE and Journal of Signal Processing
Systems for Signal, Image, and Video Technology.
Prof. Adali is a Fellow of the IEEE and the AIMBE, a Fulbright Scholar, recipient of 2013 University System of Maryland Regents' Award for Research,
and an NSF CAREER Award. She has received a number of Best Paper Awards including a 2010 IEEE Signal Processing Society Best Paper Award.
She was an IEEE Signal Processing Society Distinguished Lecturer for 2012 and 2013. Her current research interests are in the areas of
statistical signal processing, machine learning for signal processing, and applications in medical image analysis and fusion.
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