ICNC 2016 features 5 Distinguished Lectures, which are OPEN to ALL attendees of the conference and workshops.
Kwang-Cheng Chen (IEEE Fellow)
Distinguished Professor and Associate Dean, National
Taiwan University, Taiwan
Title: Socially Enabled Wireless Networks
Time: 10:00-12:00, Monday, Feb. 15, 2016
Emerging online social networks significantly change the way of content distribution and information dissemination, while the traffic of social networks has dominated Internet traffic in the mobile communication networks. Therefore, it is vital to design future wireless networks and 5G mobile communications by properly leveraging the properties of social networks. In 2013, Chen, Chiang, and Poor suggested the importance of understanding the interplay between social network and technological networks. We shall further look into the fundamentals of network science and subsequent social network analysis, and the abstract ways to utilize the nature of social networks to design wireless networks, supplying with successful engineering examples. Various aspects from analysis and system applications, particularly IoT and cellular networks will be presented in this tutorial. It shall open a new scenario and subsequently paradigm shift in the technology development of wireless networks and wireless communications to better meet the expectation from users.
Kwang-Cheng Chen received the B.S. from the National Taiwan University in 1983, and the M.S. and Ph.D from the University of Maryland, College Park, United States, in 1987 and 1989, all in electrical engineering. From 1987 to 1998, Dr. Chen worked with SSE, COMSAT, IBM Thomas J. Watson Research Center, and National Tsing Hua University, in mobile communications and networks. Since 1998, Dr. Chen has been with National Taiwan University, Taipei, Taiwan, ROC, and is the Distinguished Professor in the College of Electrical Engineering and Computer Science, National Taiwan University. He is visiting the Research Lab. of Electronics at the Massachusetts Institute of Technology, 2012-2013 and 2015-2016. Dr. Chen was with the STAG, Executive Yuan, to engineering Taiwan’s telecommunication deregulation and to plan nation’s regulator (today’s NCC) in 1990’s. He founded INPROCOMM Inc. in 2001, which was acquired by MediaTek Inc. in 2004. He has been actively involving in the organization of various IEEE conferences as General/TPC chair/co-chair, serving editorships with a few IEEE journals, and various IEEE volunteer services with IEEE Fellow Committee, IEEE VTS Fellow Evaluation Committee, Asia Pacific Board, etc. Most recently, he founds and chairs the Technical Committee on Social Networks in the IEEE Communications Society. Dr. Chen also has contributed essential technology to various international standards like IEEE 802 wireless LANs, Bluetooth, LTE (4G wireless communications) and LTE-A. He has authored and co-authored 250 IEEE papers and more than 20 granted US patents. He co-edited (with R. DeMarca) the book Mobile WiMAX published by Wiley in 2008, and authored the book Principles of Communications published by River in 2009, and co-authored (with R. Prasad) another book Cognitive Radio Networks published by Wiley in 2009. Dr. Chen is an IEEE Fellow and has received a number of awards including very recent 2011 IEEE COMSOC Wireless Communication Recognition Award, 2014 IEEE Jack Neubauer Memorial Award, 2014 IEEE COMSOC AP Outstanding Paper Award. Dr. Chen’s current research interests include wireless communications, network science, and data analytics.
Li Deng (IEEE/ASA/ISCA Fellow)
Partner Research Manager, Microsoft Research, USA
Title: Deep Learning: Propelling Recent Rapid Advances in Artificial Intelligence
Time: 10:00-12:00, Thursday, Feb. 18, 2016
Deep learning has fundamentally changed the landscape of speech recognition since year 2010 and similar revolution has taken place in image recognition and computer vision since 2012 (https://en.wikipedia.org/wiki/Deep_learning; https://en.wikipedia.org/wiki/Speech_recognition). The rapid progress in these two key areas of artificial intelligence pertaining to machine perception has given high hopes that deep learning will further thrust new advances in other areas of artificial intelligence pertaining to cognition functions of human intelligence, including natural language processing, robotics, reasoning, knowledge, decision making, etc. In this talk I will first reflect on the historical path to the transformative success of deep learning in speech recognition, after providing brief reviews of earlier studies on (shallow) neural networks and on (deep) generative models relevant to the introduction of DNNs to speech recognition. Then, an overview is given on sweeping achievements of deep learning in speech recognition since its initial success. Such achievements, summarized into six major areas, have resulted in across-the-board, industry-wide deployment of deep learning in modern speech recognition systems worldwide. The huge impact of deep learning in image recognition and computer vision is also described and analyzed in terms of the same enabling factors of big compute, big data, and innovations in deep architectures and learning methods as in speech recognition. Next, more challenging application areas of deep learning, including natural language processing, multimodal processing involving text, and deep reinforcement learning for decision making, will be selectively reviewed and analyzed. I will show examples of machine translation, contextual entity search, and automatic image captioning, where fresh ideas from deep learning, continuous-space embedding of natural language text in particular, are revolutionizing these application areas. Finally, a number of key issues and future directions of deep learning for artificial intelligence tasks are addressed and explored.
Li Deng received the Ph.D. degree from the University of Wisconsin-Madison. He was an assistant professor (1989-1992), associate professor (1992-1996) and full professor (1996-1999) at the University of Waterloo, Ontario, Canada. In 1999, he joined Microsoft Research, Redmond, USA, where currently he leads R&D of application-focused deep learning as Partner Research Manager of its Deep Learning Technology Center. Since 2000, he has also been Affiliate Full Professor at the University of Washington, Seattle. Prior to joining Microsoft, he also conducted research and taught at Massachusetts Institute of Technology, ATR Interpreting Telecom. Research Lab. (Kyoto, Japan), and HKUST. In the general areas of audio/speech/language technology, machine learning, signal and information processing, he has published over 300 refereed papers, and authored or co-authored 5 books including the latest books on Deep Learning: Methods and Applications and on Automatic Speech Recognition: A Deep-Learning Approach (Springer). He is a Fellow of the Acoustical Society of America, a Fellow of the IEEE, and a Fellow of the International Speech Communication Association. He served on the Board of Governors of the IEEE Signal Processing Society (2008-2010). More recently, he served as Editors-In-Chief for IEEE Signal Processing Magazine (2009-2011) and for IEEE/ACM Transactions on Audio, Speech and Language Processing (2012-2014).
Anna Scaglione (IEEE Fellow)
State University, USA
Title: The internet of things meets sustainable power delivery
Time: 13:30-15:30, Thursday, Feb. 18, 2016
Part of the Internet of Things is an ecosystems of electric appliances like Electric Vehicles, Smart Thermostats and Smart Lighting that will allow customers to control the environment but also potentially interact with the market of electricity directly, satisfying economically the users preferences while better exploiting the variable production from renewable energy. But unlike the internet, which is managed in a decentralized fashion, power systems reliability requires to balance the whole system with global coordination. The interaction between the customers and their variety of uses and the market forces in whole sale electricity markets is hampered by the curse of dimensionality. We will discuss the issue of sifting through big data to decide the schedule and closing the loop on a large number of transactions and discuss an approach that would allow to reason about these multitude of transactions with low order models that facilitate the market level decisions and lead to simple protocols for telemetry, control and means to study the price response of customers in real time.
Anna Scaglione (M.Sc.'95,
Ph.D. '99) is currently a professor in electrical and computer engineering at
Arizona State University. She was Professor of Electrical Engineering previously
at the University of California at Davis (2008-2014), after a six-year term at
Cornell (2001-2006). Prior to joining the engineering faculty at Cornell,
Scaglione was an assistant professor at the University of New Mexico
Dr. Scaglione’s expertise is in the broad area of statistical signal processing for communication, electric power systems and networks. Her current research focuses on studying and enabling decentralized learning and signal processing in networks of sensors. She also focuses on sensor systems and networking models for cyber security in critical infrastructure and for the demand side management and reliable energy delivery and in other aspects at the intersection between intelligent infrastructure, information systems and social networks.
Dr. Scaglione was elected an IEEE fellow in 2011. She received the 2000 IEEE Signal Processing Transactions Best Paper Award and more recently was honored for the 2013, IEEE Donald G. Fink Prize Paper Award for the best review paper in that year in the IEEE publications, her work with her student earned 2013 IEEE Signal Processing Society Young Author Best Paper Award (Lin Li).
Robert Schober (IEEE/CAE/EIC Fellow)
Alexander von Humboldt Professor, Friedrich Alexander
Title: Molecular Communication for Future Nanonetworks
Time: 13:30-15:30, Monday, Feb. 15, 2016
Molecular communication is an emerging research area offering many interesting and challenging new research problems for communication engineers, biologists, chemists, and physicists. Molecular communication is widely considered to be an attractive option for communication between nanodevices such as (possibly artificial) cells and nanosensors. Possible applications of the resulting nanonetworks include targeted drug delivery, health monitoring, environmental monitoring, and "bottom-up" manufacturing. To accommodate this exciting new and fast growing research area, IEEE and ACM have recently founded several new conferences and journals. In this lecture, we will give first a general overview of the areas of molecular communication and nanonetworking. Components of molecular communication networks, possible applications, and the evolution of the field will be reviewed. Subsequently, we will give an introduction to various molecular communication strategies such as gap junctions, molecular motors, and diffusion based molecular communication. Thereby, we will focus particularly on diffusion based molecular communication, identify the relevant basic laws of physics and discuss their implications for communication system design. One particular challenge in the design of diffusive molecular communication systems is intersymbol interference. We will discuss corresponding mitigation techniques and provide some results. Furthermore, we will present several receiver design options for diffusive molecular communication, discuss their respective advantages and disadvantages, and elaborate on the impact of external phenomena such as molecule degradation and flow. In the last part of the talk, we will discuss some research challenges in molecular communication from a communication and signal processing point of view.
Robert Schober was born in Neuendettelsau, Germany, in 1971. He received the Diplom (Univ.) and the Ph.D. degrees in electrical engineering from the University of Erlangen-Nuermberg in 1997 and 2000, respectively. From May 2001 to April 2002 he was a Postdoctoral Fellow at the University of Toronto, Canada, sponsored by the German Academic Exchange Service (DAAD). From 2002 to 2012 he was a Professor and Canada Research Chair in Wireless Communications at the University of British Columbia (UBC), Vancouver, Canada. Since January 2012 he is an Alexander von Humboldt Professor and the Chair for Digital Communication at the Friedrich Alexander University (FAU), Erlangen, Germany. His research interests fall into the broad areas of Communication Theory, Wireless Communications, and Statistical Signal Processing.
Dr. Schober received several awards for his research including the 2002 Heinz Maier–Leibnitz Award of the German Science Foundation (DFG), the 2004 Innovations Award of the Vodafone Foundation for Research in Mobile Communications, the 2006 UBC Killam Research Prize, the 2007 Wilhelm Friedrich Bessel Research Award of the Alexander von Humboldt Foundation, the 2008 Charles McDowell Award for Excellence in Research from UBC, a 2011 Alexander von Humboldt Professorship, and a 2012 NSERC E.W.R. Steacie Fellowship. In addition, he received best paper awards from the German Information Technology Society (ITG), the European Association for Signal, Speech and Image Processing (EURASIP), IEEE WCNC 2012, IEEE Globecom 2011, IEEE ICUWB 2006, the International Zurich Seminar on Broadband Communications, and European Wireless 2000. Dr. Schober is a Fellow of the IEEE, a Fellow of the Canadian Academy of Engineering, and a Fellow of the Engineering Institute of Canada.
Dr. Schober has served as Editor and Guest Editor on the Editorial Boards of several journals including the IEEE Transactions on Communications, the IEEE Journal on Selected Areas in Communications, the IEEE Transactions on Vehicular Technology, the Eurasip Journal on Advances in Signal Processing, and IEEE Sensors. He is currently the Editor-in-Chief of the IEEE Transactions on Communications.
Shuguang (Robert) Cui (IEEE Fellow)
Professor, Texas A&M University, USA
Title: Large Scale Sensing over Complex Networks
Time: 16:00-18:00, Monday, Feb. 15, 2016
Data intelligence is the core building block for any modern and future cyber-physical systems, and it involves three major aspects: data processing, data storage, and data communication. Interesting and challenging research problems could be formulated over the interactions among the above three aspects in the context of cyber-physical systems. In this talk, we focus on one such interaction between data processing and data communication, to solve a specific problem on networked large-scale sensing, where data processing has to be performed in a distributed fashion over a communication network. In particular, we seek good estimates of the randomly-varying state process in a dynamic cyber-physical system, at multiple distributed sensing nodes, each of them only having a partial observation of the overall state. We allow nodes to talk with neighbors defined over a communication graph, where we introduce a communication rate constraint on the average number of message exchanges allowed across the network per unit time. In a distributed Kalman filtering framework, we establish the consensus result to show that the respective error variance at each distributed node converges weakly in distribution. In addition, with large deviation analysis, we could show that such a distribution collapses to a Dirac measure (i.e., the error performance achieved by the ideal centralized Kalman filter) exponentially fast as we increase the network communication rate. To further satisfy more practical communication requirements, we then extend the result to the case with quantized message exchanges, with similar convergence results established. Towards the end of the talk, I will briefly mention another result on the interaction between data storage and data communication.
Shuguang (Robert) Cui received his Ph.D in Electrical Engineering from Stanford University, California, USA, in 2005. He is now a professor in Electrical and Computer Engineering at the Texas A&M University, College Station, TX. His current research interests focus on data oriented large-scale information analysis and system design, including large-scale distributed estimation and detection, information theoretical approaches for large data set analysis, complex cyber-physical system design, and cognitive communication network optimization. His research papers have been highly cited; according to the data on 2/16/2014 from Web of Science, 8 of them had been ranked within the top 10 most highly cited papers (one of them ranked No.1 and three of them ranked No.2) among all published over the same periods in the corresponding journals. In 2014, he was selected as the Thomson Reuters Highly Cited Researcher and included by ScienceWatch among the World's Most Influential Scientific Minds. He was the recipient of the IEEE Signal Processing Society 2012 Best Paper Award and two conference best paper awards. He has been serving as the TPC co-chairs for many IEEE conferences. He has also been serving as the Area Editor (feature articles) for IEEE Signal Processing Magazine, and the associate editors for IEEE Transactions on Big Data, IEEE JASC Series on Green Communications and Networking, IEEE Transactions on Signal Processing, and IEEE Transactions on Wireless Communications. He has been the elected member for IEEE Signal Processing Society SPCOM Technical Committee (2009~2014) and the elected Vice Chair for IEEE Wireless Technical Committee (2015~2016). He is a member of the Steering Committee for the new IEEE Transactions on Big Data and a member of the IEEE ComSoc Emerging Technology Committee. He was elevated to an IEEE Fellow in 2013. He is an IEEE ComSoc Distinguished Lecturer (2015-2016).