EASITC 2018 Taipei, Taiwan


EASITC

Invited Speakers


Speakers Bio


Borching Su

Borching Su

Borching Su was born in Tainan, Taiwan in 1978. He received the B.S. and M.S. degrees in electrical engineering and communication engineering, both from National Taiwan University (NTU), Taipei, Taiwan, in 1999 and 2001, respectively, and the Ph.D. degree in Electrical Engineering from the California Institute of Technology (Caltech), Pasadena, CA, USA, in 2008. He joined NextWave Broadband, Inc., San Diego, CA, USA in 2008 where he participated in physical-layer system design of the company's WiMax mobile chipset products. In August 2009, Dr. Su joined National Taiwan University and is currently an assistant professor. His current research interests include signal processing for communication systems, particularly blind channel estimation. Dr. Su received Charles H. Wilts prize from Caltech for his Ph.D. thesis on blind channel estimation.

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New Waveforms for 5G New Radio


The 5th generation wireless communication (5G) is coming into realization soon. With the three major goals, namely, enhanced mobile broadband (eMBB), massive machine type communication (mMTC), and ultra-reliable low-latency communication (URLLC), the standardization activities of the 5G New Radio (NR) lead by the 3rd Generation Partnership Project (3GPP) have been progressing toward improving many aspects of system performance to satisfy various application demands. This tutorial focuses on the physical-layer aspects of 5G, particularly transmission waveforms and multiple access This tutorial will start from a fundamental treatment of orthogonal frequency division multiplexing (OFDM) and orthogonal frequency division multiple access (OFDMA) which have been the foundation of 4G Long Term Evolution (LTE). Topics on transceiver designs of OFDM/OFDMA, including channel equalization, channel estimation, synchronization, and multiple-input-multiple-output (MIMO) integration, will be covered. Unlike previous generation transitions, where a complete new multiple access scheme was usually adopted to replace the legacy one, the 5G NR starts from 4G’s OFDMA and considers new waveforms and transceiver designs that meet various application demands. In the second part of the tutorial, several promising waveform candidates will be covered. They will be roughly categorized into filtering-based, windowing-based, and precoding based waveforms. Comparison of these candidates in various aspects will be discussed. The third part of the tutorial will address, on top of previous materials, some more selected 5G NR topics that are currently discussed in the 3GPP standard meetings. This includes beamforming of transmitted waveforms for mini-meter wave (mmWave) usage, and waveform design consideration for vehicle-to-everything (V2X).


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Jinwoo Shin

Jinwoo Shin

Jinwoo Shin is currently an associate professor at the School of Electrical Engineering at KAIST, Korea. His current major research interest is on algorithmic questions for machine learning and related fields. He obtained the Ph.D. degree from Massachusetts Institute of Technology in 2010 with George M. Sprowls (Best MIT CS PhD Thesis) Award and B.S. degrees (in Math and CS) from Seoul National University in 2001. After spending two years (2010-2012) at Algorithms & Randomness Center, Georgia Institute of Technology, one year (2012-2013) at Business Analytics and Mathematical Sciences Department, IBM T. J. Watson Research, he joined KAIST EE in Fall 2013. He received Kenneth C. Sevcik Award at ACM SIGMETRICS/Performance 2009, Best Publication Award from INFORMS Applied Probability Society 2013, Best Paper Award at ACM MOBIHOC 2013, Bloomberg Scientific Research Award 2015 and ACM SIGMETRICS Rising Star Award 2015. He has served TPCs (or reviewers) at AAAI, ICC, ICML, INFOCOM, INFORMS, MOBIHOC, NIPS, SIGMETRICS, UAI, WIOPT and AEs at IEEE/ACM Transactions on Networking, ACM Modeling and Performance Evaluation of Computing Systems.

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Deep learning approaches for optimization, estimation and communication


In the recent years, deep neural networks (DNN) have significantly improved the state of the arts for diverse machine learning problems and artificial intelligence (AI) applications. In principle, one can view DNNs as approximators of complex non-linear functions, and use them to improve traditional methods or linear models in a wide range of engineering tasks. In this talk, I will survey recent advances in this direction, i.e., how DNNs can be applied to seemingly non-DNN problems, including combinatorial optimization, compressed sensing, social networks, wired/wireless communication and collaborative filtering.


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Krishna Narayanan

Krishna Narayanan

Krishna Narayanan received the B.E. degree from Coimbatore Institute of Technology in 1992, M.S. degree from Iowa State University in 1994 and the Ph.D. degree in Electrical Engineering from Georgia Institute of Technology in 1998. Since 1998, he has been with the Department of Electrical and Computer Engineering at Texas A&M University, where he is currently the Eric D. Rubin professor. He has held visiting appointments at the University of Illinois at Urbana Champaign, Institut Eurecom, Indian Institute of Science and the University of California at Berkeley. His research interests are in coding theory, information theory, joint source-channel coding and signal processing with applications to wireless networks and data storage. His current research interests are in understanding the role of structured codes in multi-terminal information theory, universal codes for multi-user communication, spatially-coupled codes, polar codes, product codes and their variants, design of uncoordinated multiple access schemes and in exploring connections between sparse signal recovery and coding theory. He is passionate about technology-enabled teaching and innovative pedagogical approaches. He was the recipient of the NSF career award in 2001. He also received the 2006 best paper award from the IEEE technical committee for signal processing for data storage for his work on soft decision decoding of Reed Solomon codes. He currently serves as an associate editor for coding techniques for the IEEE Transactions on Information Theory. He served as the area editor (and as an editor) for the coding theory and applications area of the IEEE Transactions on Communications from 2007 until 2011. In 2014, he received the Professional Progress in Engineering award given to one outstanding alumnus of Iowa State University each year under the age of 44. He was elected as Fellow of the IEEE for contributions to coding for wireless communications and data storage. He has won several awards within Texas A&M university including the 2012 college level teaching award.

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The Peeling Decoder: Theory and Applications


The peeling decoder is a simple greedy decoder that can be used to decode classes of codes defined on graphs, such as low density parity check (LDPC) codes and low density generator matrix codes, on the erasure channel. This deceptively simple decoder suffices to design capacity achieving coding schemes for the erasure channel. In addition, the peeling decoder can also be used to design optimal universal rateless codes as shown by Luby in the design of LT codes. In part I of this two-part tutorial, we will explain the main theoretical ideas behind the analysis of the peeling decoder and the design of optimal fixed rate and rateless codes for the erasure channel. We will also discuss how the peeling decoder can be used to decode generalized LDPC codes and product codes when used with non-erasure channels. We will conclude part I with a discussion of the relationship between channel coding and syndrome source coding for the compression of sparse sources. The peeling decoder has been applied successfully not only to decoding codes on the erasure channel, but also in a variety of applications outside of main stream coding theory. In Part II of this tutorial, we will discuss applications of the peeling decoder to massive uncoordinated multiple access schemes, Sparse FFT computation (Pawar and Ramchandran's algorithm), and applications to fast pattern matching. Remarkably, the peeling decoder can be used to design optimal multiple access schemes in some cases and order-optimal algorithms for sparse transform computation and some sparse support recovery problems.


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Sennur Ulukus

Sennur Ulukus

Sennur Ulukus is a Professor of Electrical and Computer Engineering at the University of Maryland at College Park, where she also holds a joint appointment with the Institute for Systems Research (ISR). Prior to joining UMD, she was a Senior Technical Staff Member at AT&T Labs-Research. She received her Ph.D. degree in Electrical and Computer Engineering from Wireless Information Network Laboratory (WINLAB), Rutgers University, and B.S. and M.S. degrees in Electrical and Electronics Engineering from Bilkent University. Her research interests are in wireless communications, information theory, signal processing, and networks, with recent focus on private information retrieval, timely status updates over networks, energy harvesting communications, information theoretic physical layer security, and wireless energy and information transfer. Dr. Ulukus is a fellow of the IEEE, and a Distinguished Scholar-Teacher of the University of Maryland. She received the 2003 IEEE Marconi Prize Paper Award in Wireless Communications, an 2005 NSF CAREER Award, the 2010-2011 ISR Outstanding Systems Engineering Faculty Award, and the 2012 ECE George Corcoran Education Award. She is a Distinguished Lecturer of the Infomation Theory Society for 2018-2019. She is on the Editorial Board of the IEEE Transactions on Green Communications and Networking since 2016. She was an Editor for the IEEE Journal on Selected Areas in Communications–Series on Green Communications and Networking (2015-2016), IEEE Transactions on Information Theory (2007-2010), and IEEE Transactions on Communications (2003-2007). She was a Guest Editor for the IEEE Journal on Selected Areas in Communications (2015 and 2008), Journal of Communications and Networks (2012), and IEEE Transactions on Information Theory (2011). She was a general TPC co-chair of 2017 IEEE ISIT, 2016 IEEE Globecom, 2014 IEEE PIMRC, and 2011 IEEE CTW.

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Private Information Retrieval: An Information Theoretic Approach


Private information retrieval (PIR) is a canonical problem to study the privacy of users as they download content from publicly accessible databases. In PIR, a user (retriever) wishes to download data from one or more databases in such a way that no individual database can tell which data has been retrieved. PIR has originated in the computer science literature and has recently been revisited by the information theory community. The information-theoretic re formulation of the problem aims at determining the fundamental limits of the PIR problem, i.e., what is the largest number of bits that can be retrieved privately per bit of download, or equivalently, what is the minimum number of downloads needed per bit of private retrieval? This new information-theoretic approach also proposes novel PIR schemes which achieve or approach these fundamental limits. In this talk, I will describe the problem, summarize several break-through results in the history of this problem, and present some of the recent advances in this field.


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Elza Erkip

Elza Erkip

Elza Erkip an Institute Professor in the Electrical and Computer Engineering Department at New York University Tandon School of Engineering. She received the B.S. degree in Electrical and Electronics Engineering from Middle East Technical University, Ankara, Turkey, and the M.S. and Ph.D. degrees in Electrical Engineering from Stanford University, Stanford, CA, USA. Her research interests are in information theory, communication theory, and wireless communications. Dr. Erkip is a member of the Science Academy Society of Turkey and is among the 2014 and 2015 Thomson Reuters Highly Cited Researchers. She received the NSF CAREER award in 2001 and the IEEE Communications Society WICE Outstanding Achievement Award in 2016. Her paper awards include the IEEE Communications Society Stephen O. Rice Paper Prize in 2004, and the IEEE Communications Society Award for Advances in Communication in 2013. She has been a member of the Board of Governors of the IEEE Information Theory Society since 2012 where she is currently the Society President. She was a Distinguished Lecturer of the IEEE Information Theory Society from 2013 to 2014. Dr. Erkip has had many editorial and conference organization responsibilities. Some recent ones include Asilomar Conference on Signals, Systems and Computers, MIMO Communications and Signal Processing Track Chair in 2017, IEEE Wireless Communications and Networking Conference Technical Co-Chair in 2017, IEEE Journal on Selected Areas in Communications Guest Editor in 2015, and IEEE International Symposium of Information Theory General Co-Chair in 2013.

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An information theoretic perspective on web privacy


When we browse the internet, we expect that our social network identities and web activities will remain private. Unfortunately, in reality, users are constantly tracked on the internet. As web tracking technologies become more sophisticated and pervasive, there is a critical need to understand and quantify web users' privacy risk. In other words, what is the likelihood that users on the internet can be uniquely identified from their online activities? This tutorial provides an information theoretic perspective on web privacy by considering two main classes of privacy attacks based on the information they extract about a user. (i) Attributes capture the user's activities on the web and could include its browsing history or its memberships in groups. Attacks that exploit the attributes are called “fingerprinting attacks,” and usually include an active query stage by the attacker. (ii) Relationships capture the user's interactions with other users on the web such as its friendship relations on a certain social network. Attacks that exploit the relationships are called “social network de-anonymization attacks.” For each class, we show how information theoretic tools can be used to design and analyze privacy attacks and to provide explicit characterization of the associated privacy risks.


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