1. Professor Karim Abed-Merain
(University of Orléans, France)
Karim Abed-Meraim (Fellow, IEEE, Member IUF) was born in 1967. He received the State Engineer degree from the École Polytechnique, France, in 1990, the State Engineer degree from the École Nationale Supérieure des Télécommunications (ENST), Paris, France, in 1992, the M.Sc. degree from Paris XI University, Orsay, France, in 1992, and the Ph.D. degree in the field of signal processing and communications from ENST, in 1995. From 1995 to 1998, he was a Research Staff with the Electrical Engineering Department, the University of Melbourne, where he worked on several research projects related to “Blind System Identification for Wireless Communications”, “Blind Source Separation,” and “Array Processing for Communications.” From 1998 to 2012, he has been an Assistant Professor, then an Associate Professor with the Signal and Image Processing Department at Télécom ParisTech. In September 2012, he joined the University of Orléans, France (PRISME Laboratory), as a Full Professor. His research interests include signal processing for communications, subspace tracking, tensor decomposition, array processing, and statistical performance analysis. He is the author of about 550 scientific publications, including book chapters, international journal and conference papers, and patents. Pr. Karim Abed-Meraim is a Senior Area Editor of the IEEE Transactions on Signal Processing and an IEEE Fellow since 2019.
Talk: Tensor Decomposition and its application for Model Reduction in Neural Networks
The era of “Big Data”, which deals with massive datasets, has brought new analysis techniques for discovering new valuable information hidden in the data. Among these techniques is multilinear low-rank approximation (LRA) of matrices and tensors, which has recently attracted a lot of attention from engineers and researchers in the signal processing and machine learning communities. A tensor is a multidimensional array and provides a natural representation of high-dimensional data. Low-rank approximation of tensors (t-LRA) can be considered as a multiway extension of LRA of matrices (which are two-way) to higher dimensions. Generally, t-LRA is referred to as tensor decomposition which allows factorizing a tensor into a sequence of basic components. As a result, t-LRA provides a useful tool for dealing with several large-scale multidimensional problems in modern data analysis which would be, otherwise, intractable by classical methods. In this talk, a brief introduction to different tensor concepts and different tensor decomposition algorithms with illustrative application examples is first provided. Then, we focus on the application of tensor decomposition for model reduction in neural network and deep learning.
2. Professor KC Santosh
(Department of Computer Science, The University of South Dakota)
Prof. KC Santosh—a highly accomplished AI expert—is the chair of the Department of Computer Science and the founding director of the Applied AI Research Lab at the University of South Dakota (USD). He also served the National Institutes of Health as a research fellow and LORIA Research Center as a postdoctoral research scientist, in collaboration with industrial partner, ITESOFT, France. He earned his Ph.D. in Computer Science—Artificial Intelligence from INRIA Nancy Grand East Research Center (France). With funding exceeding $8 million from sources like DOD, DOE, NSF, ED, and SDBOR, he has authored 10 books and more than 250 peer-reviewed research articles, including IEEE TPAMI. He serves as an associate editor for esteemed journals such as IEEE Transactions on AI, Int. J of Machine Learning & Cybernetics, and Int. J of Pattern Recognition & Artificial Intelligence.
To name a few, Prof. Santosh is the proud recipient of the Visionary Leadership Award (University of Derby - UK, 2023) Cutler Award for Teaching and Research Excellence (USD, 2021), the President's Research Excellence Award (USD, 2019), and the Ignite Award from the U.S. Department of Health & Human Services (HHS, 2014).
Effective from Spring 2024, he has joined the NIST's AI Safety Institute Consortium, with USD being the only institution representing the state of South Dakota in this consortium.
As the founder of AI programs at USD, he has taken significant strides to increase enrolment in the graduate program, resulting in over 4,000% growth in just four years. His leadership has helped build multiple inter-disciplinary AI/Data Science related academic programs, including collaborations with Biology, Physics, Biomedical Engineering, Sustainability and Business Analytics departments. Prof. Santosh is highly motivated in academic leadership, and his contributions have established USD as a pioneer in AI programs within the state of SD.