Keynote Speakers

Prof. Sun-Yuan Kung
IEEE Fellow,
Princeton University, USA

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

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.

Dr. Bhavani Thuraisingham
Louis A. Beecherl, Jr. I, Distinguished Professor
Department of Computer Science
Executive Director of the Cyber Security Research Institute
Erik Jonsson School of Engineering and Computer Science
The University of Texas at Dallas
Bio: Dr. Bhavani Thuraisingham is the Louis A. Beecherl, Jr. Distinguished Professor of Computer Science and the Executive Director of the Cyber Security Research and Education Institute (CSI) at The University of Texas at Dallas. She is an elected Fellow of IEEE, the AAAS, the British Computer Society, and the SPDS (Society for Design and Process Science). She received several prestigious award including IEEE Computer Society's 1997 Technical Achievement Award for “outstanding and innovative contributions to secure data management”, the 2010 ACM SIGSAC (Association for Computing Machinery, Special Interest Group on Security, Audit and Control) Outstanding Contributions Award for “seminal research contributions and leadership in data and applications security for over 25 years” and the SDPS Transformative Achievement Gold Medal for her contributions to interdisciplinary research. She has unique experience working in the commercial industry (Honeywell), federal research laboratory (MITRE), US government (NSF) and academia and her 35 year career includes research and development, technology transfer, product development, program management, and consulting for the federal government. Her work has resulted in 100+ journal articles, 200+ conference papers, 100+ keynote and featured addresses, eight US patents (three pending) and fifteen books (two pending). She received the prestigious earned higher doctorate degree (DEng) from the University of Bristol England in 2011 for her published work in secure data management since her PhD. She has been a strong advocate for women in computing and has delivered featured addresses at events organized by the CRA-W (Computing Research Association) and SWE (Society for Women Engineers).


This presentation will describe our research and development efforts in assured cloud computing for the Air Force Office of Scientific Research. We have developed a secure cloud computing framework as well as multiple secure cloud query processing systems. Our framework uses Hadoop to store and retrieve large numbers of RDF triples by exploiting the cloud computing paradigm and we have developed a scheme to store RDF data in a Hadoop Distributed File System. We implemented XACML-based policy management and integrated it with our query processing strategies. For secure query processing with relational data we utilized the HIVE framework. More recently we have developed strategies for secure storage and query processing in a hybrid cloud. In particular, we have developed algorithms for query processing wherein user’s local computing capability is exploited alongside public cloud services to deliver an efficient and secure data management solution. We have also developed techniques for secure virtualization using the XEN hypervisor to host our cloud data managers as well as an RDF-based policy engine hosted on our cloud computing framework. Finally we have developed a secure social media framework hosted on our secure cloud computing framework.

The presentation will discuss our secure cloud computing framework for assured information sharing and discuss the secure social media framework. We will then discuss the relationship to big data security and privacy aspects and connect our research to Secure Internet of Things with a special emphasis on data privacy.

Prof. Xiaodong Wang
IEEE Fellow,
Columbia University, USA

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

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.

Prof. Jianhui Wang (Section Manager, Advanced Power Grid Modeling Energy Systems Division Argonne National Laboratory University of Notre Dame)

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

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.