Georgios B. Giannakis (Fellow’97) received his Diploma in Electrical Engr. from the Ntl. Tech. Univ. of Athens, Greece, 1981. From 1982 to 1986 he was with the Univ. of Southern California (USC), where he received his MSc. in Electrical Engineering, 1983, MSc. in Mathematics, 1986, and Ph.D. in Electrical Engr., 1986. He was with the U. of Virginia from 1987 to 1998, and since 1999 he has been a professor with the U. of Minnesota, where he holds a Chair in Wireless Communications, a University of Minnesota McKnight Presidential Chair in ECE, and serves as director of the Digital Technology Center. His general interests span the areas of communications, networking and statistical signal processing – subjects on which he has published more than 430 journal papers, 720 conference papers, 25 book chapters, two edited books and two research monographs (h-index 132). Current research focuses on data science and network science with applications to social, brain, and power networks with renewables. He is the (co-) inventor of 32 patents issued, and the (co-) recipient of 9 best journal paper awards from the IEEE Signal Processing (SP) and Communications Societies. He also received Technical Achievement Awards from the SP Society (2000), from EURASIP (2005), and the inaugural IEEE Fourier Tech. Field Award (2015). He is a Fellow of EURASIP, and has served the IEEE in various posts including that of a Distinguished Lecturer.
Title of The Talk: Sparsity and Low Rank for Inference of Cognitive Communication Network States
Abstract.: Viewed through a statistical inference lens, many challenges facing communication network analytics boil down to (non-) parametric regression and classification, dimensionality reduction, or clustering. Adopting such a vantage point, this keynote presentation will put forth novel learning approaches for comprehensive situation awareness of cognitive radio (CR) communication networks that include spatio-temporal sensing via RF spectrum and channel gain cartography, flagging of network anomalies, prediction of network processes, and dynamic topology inference. Key emphasis will be placed on parsimonious models leveraging sparsity, low-rank or low-dimensional manifolds, attributes that are instrumental for complexity reduction.
Sarah Harris is an Associate Professor at the University of Nevada, Las Vegas. She completed her B.S. at B.Y.U. and her M.S. and Ph.D. at Stanford University. She has worked at Hewlett Packard, Nvidia, and various other places. She worked at Harvey Mudd College as an assistant and then associate professor from 2004-2014 and joined UNLV in 2014. She also spent a year as a visiting professor at the Technische University of Darmstadt in Germany. Her research interests include embedded systems, biomedical engineering, and robotics. Outside of work, she enjoys playing music and spending with her kids.
Title of the Talk: Control algorithms for smooth prosthetic gait
Abstract: An estimated 23.6 million Americans are affected by Type II diabetes and 37,000 undergo lower limb amputation each year [CDC]. About half of these patients are prescribed a foot-ankle prosthesis, yet mobility is often severely restricted by pain and impaired walking dynamics — in turn, leading to cardiovascular disease and other comorbidities. In order to affect better health outcomes for persons with amputation, we have addressed the link between residual limb pathology and mechanical dysfunction of the prosthesis with what we call a Dynamic Intelligence System. This system allows the dynamics of a human wearer of prosthetics to enter the control loop of that prosthetic limb for the first time. This Dynamic Intelligence System mitigates the extreme environment at the prosthesis-residual limb interface by reducing the harsh impacts and oscillatory loads that cause compressive, shear, and bending stresses at the residual limb.
C.-C. Jay Kuo
Dr. C.-C. Jay Kuo received his Ph.D. degree from the Massachusetts Institute of Technology in 1987. He is now with the University of Southern California (USC) as Director of the Media Communications Laboratory and Distinguished Professor of Electrical Engineering and Computer Science. His research interests are in the areas of media processing, compression and understanding. Dr. Kuo was the Editor-in-Chief for the IEEE Trans. on Information Forensics and Security in 2012-2014. He was the Editor-in-Chief for the Journal of Visual Communication and Image Representation in 1997-2011, and served as Editor for 10 other international journals. Dr. Kuo received the 1992 National Science Foundation Young Investigator (NYI) Award, the 1993 National Science Foundation Presidential Faculty Fellow (PFF) Award, the 2010 Electronic Imaging Scientist of the Year Award, the 2010-11 Fulbright-Nokia Distinguished Chair in Information and Communications Technologies, the 2011 Pan Wen-Yuan Outstanding Research Award, the 2014 USC Northrop Grumman Excellence in Teaching Award, the 2016 USC Associates Award for Excellence in Teaching, the 2016 IEEE
Computer Society Taylor L. Booth Education Award, the 2016 IEEE Circuits and Systems Society John Choma Education Award, the 2016 IS&T Raymond C. Bowman Award, and the 2017 IEEE Leon K. Kirchmayer Graduate Teaching Award. Dr. Kuo is a Fellow of AAAS, IEEE and SPIE. He has guided 145 students to their Ph.D. degrees and supervised 27 postdoctoral research fellows. Dr. Kuo is a co-author of 260 journal papers, 900 conference papers and 14 books.
Title of the Talk: Interpretable Convolutional Neural Networks via Feedforward Design
Abstract:The superior performance of Convolutional Neural Networks (CNNs) has been demonstrated in
many applications such as image classification, detection and processing. Yet, CNN’s working principle remains a mystery. We offer an interpretable design for simple CNNs through a feedforward construction without backpropagation. A CNN is simple if it is a cascade of two networks; namely, the Conv net and the FC net. The Conv net consists of convolutional layers while the FC net contains fully connected layers. To design the Conv net, we develop a new signal transform, called the Saab (Subspace approximation with adjusted bias transform. The bias term in filter weights is chosen to annihilate nonlinearity of the activation function, which simplifies our design significantly. For the FC net design, we propose a label-guided linear least squared regression (L3SR) method. To shed light on the behavior of the FC net, we measure the cross-entropy at nodes of FC layers. The properties and performance of the traditional backpropagation design and the proposed feedforward design are compared and analyzed.
|Full Paper Submission:||28th November 2017|
|Acceptance Notification:||10th December 2017|
|Final Paper Submission:||20th December 2017|
|Early Bird Registration:||20th December 2017|
|Presentation Submission:||25th December 2017|
|Conference:||8th-10th January 2018|
• Conference Proceedings will be submitted for publication at IEEE Xplore Digital Library
• Best Paper Award will be given for each tracks
• There will be two workshops on-
i. Data Analysis and ii. IoT on Jan 10, 2018
• There will be Corporate Exhibitions and Product Display on 8th and 9th January of 2018.
• Conference Record No- 41889