Bio: Professor S.Y. Kung received his Ph.D. Degree in Electrical Engineering from Stanford University in 1977. In 1974, he was an Associate Engineer of Amdahl Corporation, Sunnyvale, CA. From 1977 to 1987, he was a Professor of Electrical Engineering-Systems of the University of Southern California, L.A. Since 1987, he has been a Professor of Electrical Engineering at the Princeton University. In addition, he held a Visiting Professorship at the Stanford University (1984); and a Visiting Professorship at the Delft University of Technology (1984); a Toshiba Chair Professorship at the Waseda University, Japan (1984); an Honorary Professorship at the Central China University of Science and Technology (1994); and a Distinguished Chair Professorship at the Hong Kong Polytechnic University since 2001. His research interests include VLSI array processors, system modelling and identification, neural networks, wireless communication, sensor array processing, multimedia signal processing, bioinformatic data mining and biometric authentication. Professor Kung has authored more than 400 technical publications and numereous textbooks, Professor Kung has co-authored more than 400 technical publications and numerous textbooks including "VLSI and Modern Signal Processing," with Russian translation, Prentice-Hall (1985), "VLSI Array Processors", with Russian and Chinese translations, Prentice-Hall (1988); "Digital Neural Networks", Prentice-Hall (1993) ; "Principal Component Neural Networks", John-Wiley (1996); and "Biometric Authentication: A Machine Learning Approach", Prentice-Hall (2004). Professor Kung is a Fellow of IEEE since 1988. He served as a Member of the Board of Governors of the IEEE Signal Processing Society (1989-1991). He was a founding member of several Technical Committees (TC) of the IEEE Signal Processing Society , including VLSI Signal Processing TC (1984), Neural Networks for Signal Processing TC (1991) and Multimedia Signal Processing TC (1998), and was appointed as the first Associate Editor in VLSI Area (1984) and later the first Associate Editor in Neural Network (1991) for the IEEE Transactions on Signal Processing. He presently serves on Technical Committees on Multimedia Signal Processing. Since 1990, he has been the Editor-In-Chief of the Journal of VLSI Signal Processing Systems. Professor Kung was a recipient of IEEE Signal Processing Society's Technical Achievement Award for his contributions on "parallel processing and neural network algorithms for signal processing" (1992); a Distinguished Lecturer of IEEE Signal Processing Society (1994) ; a recipient of IEEE Signal Processing Society's Best Paper Award for his publication on principal component neural networks (1996); and a recipient of the IEEE Third Millennium Medal (2000).

Title: Compressive Privacy Based on Joint Optimization of Differential Utility/Cost

Abstract:
With the rapidly growing internet commerce, personal data are being collected, stored, and circulated around the internet - often without the data owner's knowledge. As such, protection of privacy of internet data has become a vital research field. Conventionally, the task of data protection is left entirely to the cloud server, rendering data privacy extremely vulnerable to hacker attack or unauthorized leakage. To rectify this problem, a key is to let data owners (not cloud servers) control the data privacy.

This talk explores the rich synergy among signal processing, information theory, estimation theory, and machine learning and, thereafter, presents a novel privacy preserving methodology, named compressive privacy (CS). The objective is to build user-collaborative machine learning systems to facilitate the intended function while protecting the privacy of owner's sensitive information. It involves the joint optimization over three design spaces: (i) Feature Space (ii) Utility Subspace; and (iii) Cost Subspace (i.e. Privacy Subspace). Mathematically, the optimal query can be derived from the joint optimization formulation where the query should be chosen to simultaneously maximize the utility and minimize the cost. In order to derive a closed form analysis/solution, we recast the information theoretical criterion (such as the log-likelihood or mutual information) in terms of (differential) error covariance matrix used in the estimation theory. More exactly, the optimal query search (or feature selection) becomes a problem of maximizing a Differential Utility/Cost (DUC) ratio, a criterion function commonly adopted by economists. More exactly, DUC is defined as the ratio between Differential Utility and Differential Cost. The DUC formulation can be extended to Machine Learning applications, where the Differential Utility and Cost are characterized by the given supervised training dataset. Furthermore, the DUC optimization can be reformulated in the kernel learning models, where nonlinear kernels afford a much expanded search space to enhance the optimized DUC ratio. Simulation results based on facial image classification (utility) and reconstruction (privacy) will be demonstrated.

Acknowledgement: This material is based upon work supported in part by the Brandeis Program of the Defense Advanced Research Project Agency (DARPA) and Space and Naval Warfare System Center Pacific (SSC Pacific) under Contract No. 66001-15-C-4068.

Bio: Professor Xiaodong Wang was an assistant professor from July 1998 to December 2001 at the Department of Electrical Engineering at Texas A&M University. In January 2002, he joined the Department of Electrical Engineering at Columbia University as an assistant professor. Dr. Wang's research interests fall in the general areas of computing, signal processing, and communications. He has worked and published extensively in the areas of wireless communications, statistical signal processing, parallel and distributed computing, nanoelectronics, and quantum computing. Dr. Wang has received the 1999 NSF CAREER Award. He has also received the 2001 IEEE Communications Society and Information Theory Society Joint Paper Award.

Title: Title: Real-time Big Data - Signal Processing Perspectives

Abstract:
In many applications that involve big data, real-time or online data processing and information extraction is necessary. In this talk, I will discuss a number of signal processing aspects of real-time processing of big data through examples. The first issue is efficient data acquisition (i.e., where to sample) through active learning, which is illustrated by a fine-grained indoor localization technique with adaptively sampled RF fingerprint. The second issue is low-rate sampling (i.e., how to sample) for distributed information extraction, illustrated by a cooperative spectrum sensing system. Then I will discuss statistical inference for big data based on sequential Monte Carlo through an example of gene binding site discovery. Finally, I will discuss the application of quickest change detection to detecting cyber-attack and faults in smart grids.

Bio: Dr. Jianhui Wang is the Section Manager for Advanced Power Grid Modeling at Argonne National Laboratory. He is the Secretary of the IEEE Power & Energy Society (PES) Power System Operations Committee. He has authored/co-authored more than 150 journal and conference publications. He is an editor of Journal of Energy Engineering and Applied Energy. He received the IEEE Chicago Section 2012 Outstanding Young Engineer Award and is an Affiliate Professor at Auburn University and an Adjunct Professor at University of Notre Dame. He has also held visiting positions in Europe, Australia and Hong Kong including a VELUX Visiting Professorship at the Technical University of Denmark (DTU). Dr. Wang is the Editor-in-Chief of the IEEE Transactions on Smart Grid and an IEEE PES Distinguished Lecturer. He is the recipient of the IEEE PES Power System Operation Committee Prize Paper Award in 2015.

Title: Grid Modernization: Challenges, Opportunities, and Solutions

Abstract:
Our aging grid infrastructure faces increasing challenges from multiple sources including greater demand variability, stricter environmental regulations and growing cyber security concerns. Advanced smart grid technologies provide possible solutions to tackle these challenges. Meanwhile how to best utilize these new devices and technologies such as PMUs and electric vehicles remains a challenge by itself. In this talk, I will address various topics which span a multitude of areas including cloud computing applications, large-scale optimization and computation, and cyber security. I will present the technical issues in implementing these technologies and corresponding potential solutions.