Distinguished Lectures
Distinguished Lectures
ICNC 2019 features 4 Distinguished Lectures, which are OPEN to ALL attendees of the conference and workshops.
Yiran Chen (IEEE Fellow)
Professor, Duke University, USA.
Title: An introduction of machine learning acceleration and neuromorphic computing
Time: 13:30 - 15:30, Monday February 18, 2019
Abstract:
Fast growth of the computation cost associated with training and testing of deep neural networks (DNNs) inspired various acceleration techniques. Reducing topological complexity and simplifying data representation of neural networks are two approaches that popularly adopted in deep learning society However, the practical impacts of hardware design are often ignored in these algorithm-level techniques, such as the increase of the random accesses to memory hierarchy and the constraints of memory capacity. On the other side, the limited understanding about the computational needs at algorithm level may lead to unrealistic assumptions during the hardware designs. In this talk, we will discuss this mismatch and show how we can solve it through an interactive design practice across both software and hardware levels. We will also introduce some efforts that spent on searching for bio-inspired computing model as well as the emerging post-CMOS circuitry for improving computational efficiency of machine learning and neuromorphic computing applications.
Biography:
Yiran Chen received B.S and M.S. from Tsinghua University and Ph.D. from Purdue University in 2005. After five years in industry, he joined University of Pittsburgh in 2010 as Assistant Professor and then promoted to Associate Professor with tenure in 2014, held Bicentennial Alumni Faculty Fellow. He now is a tenured Associate Professor of the Department of Electrical and Computer Engineering at Duke University and serving as the director of NSF Industry-University Cooperative Research Center (IUCRC) for Alternative Sustainable and Intelligent Computing (ASIC) and co-director of Duke Center for Evolutionary Intelligence (CEI), focusing on the research of new memory and storage systems, machine learning and neuromorphic computing, and mobile computing systems. Dr. Chen has published one book and more than 350 technical publications and has been granted 93 US patents. He serves or served the associate editor of several IEEE and ACM transactions/journals and served on the technical and organization committees of more than 50 international conferences. He received 6 best paper awards and 12 best paper nominations from international conferences. He is the recipient of NSF CAREER award and ACM SIGDA outstanding new faculty award. He is the Fellow of IEEE.
Schahram Dustdar (member of Academia Europaea, IEEE Fellow)
Professor, TU Wien, Austria
Title: Paradigmatic Research Challenges in IoT Systems Engineering
Time: 16:00 - 18:00, Monday February 18, 2019
Abstract:
This talk explores the research challenges in the domain of IoT from multiple angles and reflects on the urgently needed collective efforts from various research communities to collaborate on those. Our approach fundamentally challenges the current thinking and understanding of scientific, technological, and political paradigms in tackling the engineering of IoT systems. We discuss technical paradigms and research challenges in the domains of Cloud and Edge Computing as well as the requirements of people in such systems. We will explore how these novel approaches impact application composition utilizing AI and Edge Computing.
Biography:
Schahram Dustdar is Professor of Computer Science heading the Distributed Systems Group at the Technical University of Vienna. From 2004-2010 he was also Honorary Professor of Information Systems at the Department of Computing Science at the University of Groningen (RuG), The Netherlands. From 1999 - 2007 he worked as the co-founder and chief scientist of Caramba Labs Software AG in Vienna (acquired by Engineering NetWorld AG), a venture capital co-funded software company focused on software for collaborative processes in teams. Caramba Labs was nominated for several (international and national) awards: World Technology Award in the category of Software (2001); Top-Startup companies in Austria (Cap Gemini Ernst & Young) (2002); MERCUR Innovationspreis der Wirtschaftskammer (2002).
From Dec 2016 until Jan 2017 he was a Visiting Professor at the University of Sevilla, Spain and from January until June 2017 he was a Visiting Professor at UC Berkeley, USA. He is co-Editor-in-Chief of the new ACM Transactions on the Internet of Things as well as Editor-in-Chief of Computing (Springer). He is an Associate Editor of IEEE Transactions on Services Computing, IEEE Transactions on Cloud Computing, ACM Transactions on the Web, and ACM Transactions on Internet Technology, as well as on the editorial board of IEEE Internet Computing and IEEE Computer. Dustdar is recipient of the ACM Distinguished Scientist award (2009), the IBM Faculty Award (2012), an elected member of the Academia Europaea: The Academy of Europe, where he is chairman of the Informatics Section, as well as an IEEE Fellow (2016).
Zhu Han (IEEE Fellow)
Professor, University of Houston, USA
Title: Signal Processing for Big Data Analytics
Time: 10:00 - 12:00, Monday February 18, 2019.
Abstract:
The aim of this tutorial is to bring together signal processing engineers, computer and information scientists, applied mathematicians and statisticians, as well as systems engineers to carve out the role that analytical and experimental engineering has to play in Big Data research and development. This proposal will emphasize on signal analytics, networking, computation, optimization, as well as systems engineering aspects of Big Data. There are four main objectives. The first objective is to provide an introduction to the big data paradigm, from the signal processing perspective. The second objective is to introduce the key techniques to enable signal processing for big data in a comprehensive way. The third objective is to provide numerical datasets, and illuminate how signal processing approaches can be addressed to wireless datasets. The fourth objective is to present the state-of-the-art big data applications. This will include classifications of the different schemes and the technical details in each scheme.
Biography:
Zhu Han received the B.S. degree in electronic engineering from Tsinghua University, in 1997, and the M.S. and Ph.D. degrees in electrical engineering from the University of Maryland, College Park, in 1999 and 2003, respectively. From 2000 to 2002, he was an R&D Engineer of JDSU, Germantown, Maryland. From 2003 to 2006, he was a Research Associate at the University of Maryland. From 2006 to 2008, he was an assistant professor in Boise State University, Idaho. Currently, he is a John and Rebecca Moores Professor in Electrical and Computer Engineering Department as well as Computer Science Department at University of Houston, Texas. His research interests include security, wireless resource allocation and management, wireless communications and networking, game theory, and wireless multimedia. Dr. Han is an NSF CAREER award recipient 2010. Dr. Han has several IEEE conference best paper awards, and winner of 2011 IEEE Fred W. Ellersick Prize, 2015 EURASIP Best Paper Award for the Journal on Advances in Signal Processing and 2016 IEEE Leonard G. Abraham Prize in the field of Communications Systems (Best Paper Award for IEEE Journal on Selected Areas on Communications). Dr. Han has been IEEE fellow since 2014 and IEEE Distinguished Lecturer since 2015. Dr. Han is 1% highly cited researcher according to Web of Science since 2017.
Liuqing Yang (IEEE Fellow)
Professor, Colorado State University, USA
Title: Collective Intelligence for Situational Awareness in Autonomous Driving
Time: 10:00 - 12:00, Thursday, February 21, 2019
Abstract:
In recent years, autonomous driving has been advocated as a promising technique to address the increasing burden and safety concerns in our transportation system. Despite the many exciting recent achievements, repeated accidents in public road tests have brought general concerns on the technology readiness regarding autonomous driving. With a deeper look into the current framework, we find that the major bottleneck lies not in the "intelligence" technique available, but in the "machine" itself. More specifically, for a single vehicle, its physical limitation in space directly constrains its information collection and knowledge acquisition capability, which in turn sets the boundary on the intelligence it can achieve. To overcome this limitation, in this talk, we will introduce an innovative and comprehensive framework of multi-vehicle cooperation to greatly enhance the intelligence of autonomous vehicles. We will also communications and networking innovations necessary to realize such a framework.
Biography:
Prof. Liuqing Yang received the Ph.D. degree from the University of Minnesota, Minneapolis, MN, USA, in 2004. Her main research interests include theory and application of signal processing and data analytics, subjects on which she has published more than 290 technical papers, 3 books and 3 book chapters. She received the Office of Naval Research Young Investigator Program Award in 2007, the National Science Foundation Career Award in 2009, the IEEE GLOBECOM Outstanding Service Award in 2010, the George T. Abell Outstanding Mid-Career Faculty Award and the Art Corey Outstanding International Contributions Award at CSU in 2012 and 2016 respectively, and Best Paper Awards at IEEE ICUWB'06, ICCC'13, ITSC'14, GLOBECOM'14, ICC'16, WCSP'16, ICCS'18 and GLOBECOM'18. She is currently the Editor in Chief for IET Communications.