Keynote Speakers

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Enis Erkel

Head of Research and Development
Turk Telekom Group

Communications and Networking Industry Challenges

Abstract

On today's networks, applications, devices and the data traffic they generate are increasing exponentially. Fast-growing machine-to-machine services add more data and create further challenges in data traffic management. As a result, network complexity is growing rapidly, and to keep up, operators will need to invest more in several key areas while also innovating on cost reduction. Two key technologies being explored by operators are Software Defined Networking and Network Virtualization, with the potential to reduce network complexity and relieve pressure on OPEX and CAPEX costs. A third, emerging area is energy management as a pillar of network management. With operator OPEX costs consuming a very high percentage of revenues relative to other industries (up to 80%), there is signifcant opportunity to innovate in energy efficiency and management. Additionally, closer relationships between utilities and telecoms may be mutually beneficial, as inefficient power distribution networks can learn from today's complex communication networks.

Short Biography

Enis Erkel joined Turk Telekom (TT) Group in 2009 as Head of R&D. TT Group comprises 3 operators and 6 technology companies with over 900 R&D engineers. He has established strong partnerships with leading universities and research centers to jointly define and develop next generation ICT technologies, and launched over 50 leading-edge R&D projects during the first two years.

Previously, Enis was one of the founding managers of LG-Nortel, a joint venture telecommunications equipment company between LG and Nortel, established in 2005 in South Korea. He held key roles in the company including Vice President of Technology, providing leadership for a 1000-staff R&D organization.

Between 2000 and 2005, Enis was Vice President in charge of Nortel's Carrier voice and data solutions business in Asia, and later in Eastern Europe. During his Asia leadership role, he guided two R&D labs in China to become global R&D centers. He joined Bell-Northern Research (BNR) in USA in 1985 and held successive R&D leadership positions within BNR with increasing responsibility.

Enis holds a Bachelor of Science in Electrical Engineering, with graduate studies in Computer Sciences and is a graduate of the Executive Management Program at Kenan-Flagler Business School of UNC. He was the sole recipient of Nortel's prestigious "President's Award of Excellence in Leadership" in 1999.

He has been a Board Member of two leading EUREKA clusters in telecommunications and energy fields: Celtic Plus, and Eurogia+.

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Georgios B. Giannakis

ADC Chair Professor in Wireless Telecommunications
Department of Electrical and Computer Engineering
Director of Digital Technology Center
University of Minnesota, USA
Website

Sparsity and Low Rank for Robust Social Data Analytics and Networking

Abstract

The information explosion propelled by the advent of personal computers, the Internet, and the global-scale communications has rendered statistical learning from `Big Data' increasingly important for analysis and processing. Along with data adhering to postulated models, present in large volumes of data are also those that do not - what are referred to as outliers or anomalies. In this talk, I will start with an approach to outlier-resilient principal component analysis, which establishes a neat link between the seemingly unrelated notions of sparsity and robustness to outliers, even when the signals involved are not sparse. I will argue that controlling sparsity of model residuals leads to statistical learning algorithms that are computationally affordable and universally robust. The impact of these ideas will be demonstrated in applications as diverse as identification of aberrant responses in personality assessment surveys, and unveiling communities in social networks, as well as intruders from video surveillance data. In the second part of the talk, I will switch focus towards the important task of unveiling and mapping-out network anomalies given link-level traffic measurements. Leveraging the low intrinsic-dimensionality of end-to-end network flows and the sparse nature of anomalies, I will show how to construct an estimated map of anomalies in real time to aid in monitoring the network health state. If time allows, I will finally highlight additional application domains that include predicting network-wide path latencies, and load curve cleansing and imputation -- a critical task in green grid analytics and energy management with renewables.

Short Biography

G. B. Giannakis (IEEE 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. Since 1999 he has been a professor with the Univ. of Minnesota, where he now holds an ADC Chair in Wireless Telecommunications in the ECE Department, 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 350 journal papers, 550 conference papers, 20 book chapters, two edited books and two research monographs (h-index 103). Current research focuses on compressive sensing, cognitive radios, cross-layer designs, wireless sensors, social and power grid networks. He is the (co-)inventor of 21 patents issued, and the (co-) recipient of 8 best paper awards from the IEEE Signal Processing (SP) and Communications Societies, including the G. Marconi Prize Paper Award in Wireless Communications. He also received Technical Achievement Awards from the SP Society (2000), from EURASIP (2005), a Young Faculty Teaching Award, and the G. W. Taylor Award for Distinguished Research from the University of Minnesota. He is a Fellow of EURASIP, and has served the IEEE in a number of posts, including that of a Distinguished Lecturer for the IEEE-SP Society.

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Gerhard Kramer

Alexander von Humboldt Professor
Head of the Institute for Communications Engineering
Technische Universität München, Germany
Website

Short Message Noisy Network Coding

Abstract

Network coding is a method for cooperatively communicating across networks. An extension called Noisy Network Coding (NNC) is a digital and compression-based relaying strategy that has several nice information-theoretic properties. For example, the method extends random network coding from classic networks to wireless networks, and it achieves rates within a reasonable gap of cut bounds. This talk reviews the development of the NNC strategy and presents recent improvements concerning random coding and decoding. The talk is based on joint work with Jie Hou of TUM.

Short Biography

Gerhard Kramer is Alexander von Humboldt Professor at the Technische Universität München (TUM). He received the B.Sc. and M.Sc. degrees from the University of Manitoba, and the Dr. sc. techn. degree from the ETH Zürich in 1998. From 1998 to 2000, he was with Endora Tech AG in Basel. From 2000 to 2008 he was with the Math Center, Bell Labs, Murray Hill. He joined USC in Los Angeles in 2009 and TUM in 2010.

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