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FAU College of Engineering and Computer Science
Distinguished Lecture Series
Tülay Adali, Ph.D.
Distinguished University Professor of Computer Science and Electrical Engineering, University of Maryland, Baltimore County
Guest Speaker
Mon., Feb. 19
3 to 4 p.m.
Florida Atlantic University
777 Glades Road, EE 303
Boca Raton Campus
Videoconferenced to the Dania Beach, SeaTech site, ST 259, and Harbor Branch, LE 103.
Data Fusion through Matrix and Tensor Factorizations: Uniqueness, Diversity, and Interpretability
Fusion of multiple sets of data, either of the same type as in multiset data or of different types and nature as in multi-modality data, is inherent to many problems in engineering and computer science. In data fusion, since most often, very little is known about the relationship of the underlying processes that give rise to such data, it is desirable to minimize the modeling assumptions, and at the same time, to maximally exploit the interactions within and across the multiple sets of data. This is one of the reasons for the growing importance of data-driven methods in data fusion tasks. Models based on matrix or tensor decompositions allow data sets to remain in their most explanatory form while admitting a broad range of assumptions among their elements. This talk will provide an overview of the main approaches that have been successfully applied for fusion of multiple datasets with a focus on the interrelated concepts of uniqueness, diversity, and interpretability. Diversity refers to any structural, numerical, or statistical inherent property or assumption on the data that contributes to the identifiability of the model, and for multiple datasets, provides the link among these datasets. Hence, diversity enables uniqueness, which is key to interpretability, the ability to attach a physical meaning to the final decomposition. The importance of these concepts as well as the challenges that remain are highlighted through a number of practical examples.
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.

She has been active in conference and workshop organizations. She was the general or technical co-chair of the IEEE Machine Learning for Signal Processing (MLSP) and Neural Networks for Signal Processing Workshops 2001−2008, and helped organize a number of conferences including the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). She was the chair of the technical committee on MLSP, 2003−2005 and 2011−2013, and the Technical Program Co-Chair for ICASSP 2017. She is the Special Sessions Chair for ICASSP 2018. She has served or currently serving on a number of editorial boards including the Proceedings of the IEEE.

Prof. Adali is a Fellow of the IEEE and the AIMBE, a Fulbright Scholar, and an IEEE Signal Processing Society Distinguished Lecturer. She is the recipient of a 2013 University System of Maryland Regents' Award for Research, an NSF CAREER Award, and a number of paper awards including the 2010 IEEE Signal Processing Society Best Paper Award. Her current research interests are in the areas of statistical signal processing, machine learning, and applications in medical image analysis and fusion.
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