Distinguished Lecturer講演会のご案内 IEEE Signal Processing Society Kansai Chapter Chair 河原 達也（京都大学） Vice Chair 中谷 智広（NTT） IEEE SPS Kansai Chapterでは，下記のように Distinguished Lecturer 講演会を 開催します．皆様ふるってご参加ください． 記 日程：2017年9月22日(金) 13:30～15:00 場所：京都大学 総合研究7号館 1階 第1講義室（606-8501 京都市左京区吉田本町） (アクセスのご案内) 講演者 ：Prof. Tulay Adali (University of Maryland Baltimore County) 講演題目：Multiset and Multimodal Data Fusion: Benefits of Fully Exploiting Diversity (詳しくは以下の講演概要をご覧ください)
Successful fusion of information from multiple sets of data is key 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 has been the main reason for the growing importance of data-driven methods, and in particular those based on matrix and tensor factorizations. A key concept in such decompositions is that of “diversity”. Diversity refers to any structural, numerical, or statistical inherent property or assumption on the data that contributes to the identifiability of the model. In the presence of multiple datasets, diversity establishes the link among them and is thus the key and enabling factor to data fusion.
In this talk, the main theoretical concepts for data fusion using matrix and tensor decompositions are reviewed starting with the concept of diversity, which enables identifiability. A number of models based on independent component analysis (ICA) and its recent extension to multiple datasets independent vector analysis (IVA) are discussed along with those based on basic tensor decompositions. Finally, the link between the theoretical results and practice is established by addressing interpretability and key implementation issues. A number of examples from multiple domains are presented to illustrate the concepts.
Tulay 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 very 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 has served or currently serving on numerous editorial boards and technical committees of the IEEE Signal Processing Society (SPS). She was the chair of the IEEE SPS Technical Committee on MLSP, 2003-2005 and 2011-2013, and the Technical Program Chair for ICASSP 2017. She is the Special Sessions Chair for ICASSP 2018.
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.