In many fields today, such as neuroscience, remote sensing, computational social science, and physical sciences, multiple sets of data are readily available. Matrix and tensor factorizations enable joint analysis, i.e., fusion of these multiple datasets such that they can fully interact and inform each other while also minimizing the assumptions placed on their inherent relationships. A key advantage of these methods is the direct interpretability of their results. This talk presents an overview of the main models that have been successfully used for fusion of multiple datasets using independent component analysis (ICA), and its generalization to multiple datasets, independent vector analysis (IVA) with examples in fusion of medical imaging data. A number of important challenges and future directions of research are addressed for solutions using not only ICA and IVA but also tensors and other matrix factorizations.
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 assisted in the organization of a number of international conferences and workshops including the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) as technical chair in 2017, special sessions chair in 2018, and for 2024, and the IEEE Machine Learning for Signal Processing (MLSP) and Neural Networks for Signal Processing Workshops as general and technical chair, 2001−2008. She has served or is currently serving on numerous boards and technical committees of the IEEE Signal Processing Society (SPS), and is currently the Vice President for Technical Directions of the IEEE SPS. Prof. Adali is a Fellow of the IEEE and the AIMBE, a Fulbright Scholar, and a Distinguished Lecturer. She is the recipient of a 2020-2021 Humboldt Research Award, 2010 IEEE SPS Best Paper Award, 2013 University System of Maryland Regents' Award for Research, and an NSF CAREER Award. Her current research interests are in the areas of statistical signal processing, machine learning, and their applications with emphasis on applications in medical image analysis and fusion.