Tülay Adali

Data Fusion through Matrix and Tensor Factorizations: Uniqueness, Diversity, and Interpretability

Abstract: 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 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.

Mérouane Debbah

Artificial Intelligence for 5G : Challenges and Opportunities

Abstract: Mobile cellular networks are becoming increasingly complex to manage while classical deployment/optimization techniques are cost-ineffective and thus seen as stopgaps. This is all the more difficult considering the extreme constraints of 5G networks in terms of data rate (more than 10 Gb/s), massive connectivity (more than 1000000 devices per km2), latency (under 1ms) and energy efficiency (a reduction by a factor of 100 with respect to 4G network). Unfortunately, the development of adequate solutions is severely limited by the scarcity of the actual resources (energy, bandwidth and space). Recently, the community has turned to a new resource known as Artificial Intelligence at all layers of the network to exploit the increasing computing power afforded by the improvement in Moore's law in combination with the availability of huge data in 5G networks. This is an important paradigm shift which considers the increasing data flood/huge number of nodes as an opportunity rather than a curse. In this talk, we will discuss through various examples how the recent advances in big data algorithms can provide an efficient framework for the design of 5G Intelligent Networks.

Mérouane Debbah entered the Ecole Normale Supérieure de Cachan (France) in 1996 where he received his M.Sc and Ph.D. degrees respectively. He worked for Motorola Labs (Saclay, France) from 1999-2002 and the Vienna Research Center for Telecommunications (Vienna, Austria) until 2003. From 2003 to 2007, he joined the Mobile Communications department of the Institut Eurecom (Sophia Antipolis, France) as an Assistant Professor. Since 2007, he is a Full Professor at CentraleSupelec (Gif-sur-Yvette, France). From 2007 to 2014, he was the director of the Alcatel-Lucent Chair on Flexible Radio. Since 2014, he is Vice-President of the Huawei France R&D center and director of the Mathematical and Algorithmic Sciences Lab. His research interests lie in fundamental mathematics, algorithms, statistics, information & communication sciences research. He is an Associate Editor in Chief of the journal Random Matrix: Theory and Applications and was an associate and senior area editor for IEEE Transactions on Signal Processing respectively in 2011-2013 and 2013-2014. Mérouane Debbah is a recipient of the ERC grant MORE (Advanced Mathematical Tools for Complex Network Engineering). He is a IEEE Fellow, a WWRF Fellow and a member of the academic senate of Paris-Saclay. He has managed 8 EU projects and more than 24 national and international projects. He received 16 best paper awards, among which the 2007 IEEE GLOBECOM best paper award, the Wi-Opt 2009 best paper award, the 2010 Newcom++ best paper award, the WUN CogCom Best Paper 2012 and 2013 Award, the 2014 WCNC best paper award, the 2015 ICC best paper award, the 2015 IEEE Communications Society Leonard G. Abraham Prize, the 2015 IEEE Communications Society Fred W. Ellersick Prize, 2016 IEEE Communications Society Best Tutorial paper award and the 2016 European Wireless Best Paper Award as well as the Valuetools 2007, Valuetools 2008, CrownCom2009, Valuetools 2012 and SAM 2014 best student paper awards. He is the recipient of the Mario Boella award in 2005, the IEEE Glavieux Prize Award in 2011 and the Qualcomm Innovation Prize Award in 2012.

Nikos Sidiropoulos

Tensors and Probability: An Intriguing Union

Abstract: We reveal an interesting link between tensors and multivariate statistics. The rank of a multivariate probability tensor can be interpreted as a nonlinear measure of statistical dependence of the associated random variables. Rank equals one when the random variables are independent, and complete statistical dependence corresponds to full rank; but we show that rank as low as two can already model strong statistical dependence. In practice we usually work with random variables that are neither independent nor fully dependent -- partial dependence is typical, and can be modeled using a low-rank multivariate probability tensor. Directly estimating such a tensor from sample averages is impossible even for as few as ten random variables taking ten values each -- yielding a billion unknowns; but we often have enough data to estimate lower-order marginalized distributions. We prove that it is possible to identify the higher-order joint probabilities from lower order ones, provided that the higher-order probability tensor has low-enough rank, i.e., the random variables are only partially dependent. We also provide a computational identification algorithm that is shown to work well on both simulated and real data. The insights and results have numerous applications in estimation, hypothesis testing, completion, machine learning, and system identification. Low-rank tensor modeling thus provides a `universal’ non-parametric (model-free) alternative to probabilistic graphical models.

Nikos Sidiropoulos received the Diploma in Electrical Engineering from the Aristotelian University of Thessaloniki, Greece, and M.S. and Ph.D. degrees in Electrical Engineering from the University of Maryland–College Park, in 1988, 1990 and 1992, respectively. He served as Assistant Professor at the University of Virginia (1997-1999); Associate Professor at the University of Minnesota–Minneapolis (2000-2002); Professor at the Technical University of Crete, Chania–Crete, Greece (2002-2011); and Professor at the University of Minnesota–Minneapolis (2011-), where he holds an ADC Chair in Digital Technology (2015-). His research interests are in signal processing, communications, optimization, tensor decomposition, and factor analysis. His current research focuses primarily on signal and tensor analytics and optimization-based algorithms, with applications in machine learning and communications. He received the NSF/CAREER award in 1998, and the IEEE Signal Processing Society (SPS) Best Paper Award in 2001, 2007, and 2011. He served as IEEE SPS Distinguished Lecturer (2008-2009), and as Chair of the IEEE Signal Processing for Communications and Networking Technical Committee (2007-2008), and was recently elected Vice President - Membership of the IEEE Signal Processing Society. He received the 2010 IEEE Signal Processing Society Meritorious Service Award, and the 2013 Distinguished Alumni Award from the University of Maryland, Dept. of ECE. He is a Fellow of IEEE (2009) and a Fellow of EURASIP (2014).