DATAIA Seminars

DATAIA Seminars | « Matrix and Tensor Factorizations for Data Fusion » - Tülay Adali

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DATAIA Seminars - Tülay Adali
Date de tri
Lieu de l'événement
Amphitheater Peugeot, CentraleSupélec and online


As part of its scientific activities, the DATAIA Institute organises throughout the year seminars aimed at discussing about AI.
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Tülay Adali, Distinguished Professor in the Department of Computer Science and Electrical Engineering at UMBC, will present its work on the theme "Matrices and Tensor Factorizations for Data Fusion: Focus on Model Match, Interpretability, and Reproducibility".

Abstract : In many fields today, such as neuroscience, remote sensing, computational social science, and the physical sciences, multiple datasets are readily available. Matrix and tensor factorizations allow joint analysis, i.e., fusion, of these multiple datasets so that they can interact and inform each other while minimizing assumptions about 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 merging multiple data sets. Examples based on independent component and independent vector analysis as well as canonical polyadic decomposition are discussed in more detail with examples of neuroimaging data fusion. The importance of computational reproducibility is also discussed, with emphasis on its relationship to pattern matching and interpretability.

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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 organizations. She served or will serve as technical chair, 2017, special sessions chair, 2018, 2024, publicity chair, 2000, 2005, for the IEEE International Conference on Acoustics, Speech, and  Signal Processing (ICASSP), general/technical chair for the IEEE Machine Learning for Signal Processing (MLSP) and Neural Networks for Signal Processing Workshops 2001−2009, 2014, 2023. She served or is currently serving on numerous boards and technical committees of the IEEE Signal Processing Society (SPS). She was the Chair of the NNSP/MLSP Technical Committee, 2003–2005 and 2011–2013, and the SPS Vice President for Technical Directions 2019−2022. She is currently the Chair-Elect for the IEEE Brain Initiative.  

Prof. Adali is a Fellow of the IEEE and the AIMBE, a Fulbright Scholar, and an IEEE SPS Distinguished Lecturer. She is the recipient of a Humboldt Research Award, an IEEE SPS Best Paper Award, SPIE Unsupervised Learning and ICA Pioneer Award, the 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. 

Practical information
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  • The seminar will take place on Wednesday, November 9, 2022, from 11:00 am to 12:00 pm, at the Amphitheater Peugeot of CentraleSupélec (Bouygues building - 9 rue Joliot Curie, 91190 Gif-sur-Yvette). A cocktail reception will be served.
  • It will also be broadcasted live.
  • Registration is mandatory (subject to availability)!
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