Technical Program

Day 1, Dec. 14, 2016

Day 2, Dec. 15, 2016

Day 3, Dec. 16, 2016

Prof. John E. Hopcroft

Title: How a simple question leads to fundamental research

Abstract: This talk will be about how a simple question led to fundamental research in social networks by a faculty member here in China. It will then ask some simple questions about deep learning that need to be answered.

Bio: John E. Hopcroft is the IBM Professor of Engineering and Applied Mathematics in Computer Science at Cornell University. He received his BS (1961) from Seattle University and his M.S. (1962) and Ph.D. (1964) in electrical engineering from Stanford University. His research centers on theoretical aspects of computer science. He served as dean of Cornell University’s College of Engineering from 1994 until 2001. He is a member of the National Academy of Sciences, of the National Academy of Engineering, and a fellow of the American Academy of Arts and Sciences, the American Association for the Advancement of Science, the Institute of Electrical and Electronics Engineers, the Association of Computing Machinery, and the Society of Industrial and Applied Mathematics. In 1986 he was awarded the A. M. Turing Award for his research contributions. In 1992, he was appointed by President George Bush to the National Science Board, which oversees the National Science Foundation, and served through May 1998. He received the IEEE Harry Goode Memorial Award in 2005, the Computing Research Association’s Distinguished Service Award in 2007, the ACM Karl V. Karlstrom Outstanding Educator Award in 2009, the IEEE Von Neumann Medal in 2010, and China’s Friendship Gold Metal in 2016, China’s highest recognition for a foreigner. He has honorary degrees from Seattle University, the National College of Ireland, the University of Sydney, St Petersburg State University, Beijing University of Technology, HKUST and is an honorary professor of the Beijing Institute of Technology, Shanghai Jiao Tong University, Chongqing University, and Yunnan University. He serves on Microsoft Technical Advisory Board for Research Asia, and the advisory boards of IIIT Delhi and Seattle University’s College of Engineering. The Chinese Academy of Sciences has designated him as an Einstein professor.

Prof. Babak Hassibi

Title: Simple Algorithms and Guarantees for Low Rank Matrix Completion over $GF(2)$

Abstract: Let $X^*$ be a $n_1 \cdot n_2$ matrix with entries in $F_2$ and rank $r < min(n_1, n_2)$ (often $r \leq min(n_1, n_2)$). We consider the problem of reconstructing $X^*$ given only a subset of its entries. This problem has recently found numerous applications, most notably in network and index coding, where finding optimal linear codes (over some field $Fq$) can be reduced to finding the minimum rank completion of a matrix with a subset of revealed entries. The problem of matrix completion over reals also has many applications and in recent years several polynomial-time algorithms with provable recovery guarantees have been developed. However, to date, such algorithms do not exist in the finite-field case. We propose a linear algebraic algorithm, based on inferring low-weight relations among the rows and columns of $X^*$, to attempt to complete $X^*$ given a random subset of its entries. We establish conditions on the row and column spaces of $X^*$ under which the algorithm runs in polynomial time (in the size of $X^*$) and can successfully complete $X^*$ with high probability from a vanishing fraction of its entries. We then propose a linear programming-based extension of our basic algorithm, and evaluate it empirically.

Bio: Babak Hassibi has been with the California Institute of Technology since 2001, where he is currently the inaugural Mose and Lillian S. Bohn Professor of Electrical Engineering. From 2011 to 2016 he was the Gordon M Binder/Amgen Professor of Electrical Engineering and during 2008-2015 he was Executive Officer of Electrical Engineering, as well as Associate Director of Information Science and Technology. Prior to Caltech, he was a Member of the Technical Staff in the Mathematical Sciences Research Center at Bell Laboratories, Murray Hill, NJ and obtained his PhD degree from Stanford University in 1996 and his BS degree from the University of Tehran in 1989. His research interests span various aspects of information theory, communications, signal processing, control and machine learning. He is an ISI highly cited author in Computer Science and, among other awards, is the recipient of the US Presidential Early Career Award for Scientists and Engineers (PECASE) and the David and Lucille Packard Fellowship in Science and Engineering.

Prof. Masahito Hayashi

Title: Secure wireless communication under spatial and local Gaussian noise assumptions

Abstract: We consider wireless communication between Alice and Bob when the intermediate space between Alice and Bob is controlled by Eve. That is, Eve is allowed to inject artificial noise to Bob's detection. In this situation, using backward reconciliation, we propose a protocol to generate secure keys between Alice and Bob under the assumption that Eve's detector has a Gaussian noise and Eve is out of Alice's neighborhood. Also, we give a necessarily and sufficient condition to generate the agreed secure keys via our protocol. In our protocol, the security criteria are quantitatively guaranteed even with finite block-length code when Alice and Bob do not detect the existence of eavesdropping. The contents are available from arXiv:1604.00635.

Bio: Masahito Hayashi was born in Japan in 1971. He received the B.S. degree from the Faculty of Sciences in Kyoto University, Japan, in 1994 and the M.S. and Ph.D. degrees in Mathematics from Kyoto University, Japan, in 1996 and 1999, respectively.

He worked in Kyoto University as a Research Fellow of the Japan Society of the Promotion of Science (JSPS) from 1998 to 2000, and worked in the Laboratory for Mathematical Neuroscience, Brain Science Institute, RIKEN from 2000 to 2003, and worked in ERATO Quantum Computation and Information Project, Japan Science and Technology Agency (JST) as the Research Head from 2000 to 2006. He also worked in the Superrobust Computation Project Information Science and Technology Strategic Core (21st Century COE by MEXT) Graduate School of Information Science and Technology, The University of Tokyo as Adjunct Associate Professor from 2004 to 2007. In 2006, he published the book "Quantum Information: An Introduction" from Springer, whose revised version was published as "Quantum Information Theory: Mathematical Foundation" from Graduate Texts in Physics, Springer in 2016. He worked in the Graduate School of Information Sciences, Tohoku University as Associate Professor from 2007 to 2012. In 2012, he joined the Graduate School of Mathematics, Nagoya University as Professor. He also worked in Centre for Quantum Technologies, National University of Singapore as Visiting Research Associate Professor from 2009 to 2012 and as Visiting Research Professor from 2012 to now. In 2011, he received Information Theory Society Paper Award (2011) for Information-Spectrum Approach to Second-Order Coding Rate in Channel Coding. In 2016, he received the Japan Academy Medal from the Japan Academy and the JSPS Prize from Japan Society for the Promotion of Science. In 2016, he published other two books "Group Representation for Quantum Theory" an "A Group Theoretic Approach to Quantum Information" from Springer.

He is on the Editorial Board of International Journal of Quantum Information and International Journal On Advances in Security. His research interests include classical and quantum information theory, classical and quantum statistical inference, and classical and quantum information security.

Prof. Vincent Tan

Title: Fundamental Limits of Adversarial Top-K ranking

Abstract: We study the top-K ranking problem where the goal is to recover the set of top-K ranked items out of a large collection of items based on partially revealed preferences. We consider an adversarial crowdsourced setting where there are two population sets, and pairwise comparison samples drawn from one of the populations follow the standard Bradley-Terry-Luce model (i.e., the chance of item i beating item j is proportional to the relative score of item i to item j), while in the other population, the corresponding chance is inversely proportional to the relative score. When the relative size of the two populations is known, we characterize the minimax limit on the sample size required (up to a constant) for reliably identifying the top-K items, and demonstrate how it scales with the relative size. Moreover, by leveraging a tensor decomposition method for disambiguating mixture distributions, we extend our result to the more realistic scenario in which the relative population size is unknown, thus establishing an upper bound on the fundamental limit of the sample size for recovering the top-K set.

Joint work with Changho Suh (KAIST) and Renbo Zhao (NUS)

Bio: Vincent Y. F. Tan was born in Singapore in 1981. He is an Assistant Professor in the Department of Electrical and Computer Engineering (ECE) and the Department of Mathematics at the National University of Singapore (NUS). He received the B.A. and M.Eng. degrees in Electrical and Information Sciences from Cambridge University in 2005. He received the Ph.D. degree in Electrical Engineering and Computer Science (EECS) from the Massachusetts Institute of Technology in 2011. He was a postdoctoral researcher in the Department of ECE at the University of Wisconsin-Madison in 2011 and following that, a scientist at the Institute for Infocomm Research (I2R), A*STAR, Singapore from 2012 to 2013. His research interests include information theory, machine learning and statistical signal processing.

Dr. Tan has received several awards including the MIT EECS Jin-Au Kong outstanding doctoral thesis prize in 2011; the A*STAR Philip Yeo prize for outstanding achievements in research in 2011; and the NUS Young Investigator Award in 2014. He was also placed in the NUS Faculty of Engineering Teaching commendation list in 2016. He has authored a research monograph titled “Asymptotic Estimates in Information Theory with Non-Vanishing Error Probabilities” in the Foundations and Trends® in Communications and Information Theory Series (NOW Publishers). A Senior Member of the IEEE, he served as a member of the IEEE “Machine Learning for Signal Processing” Technical Committee within the IEEE Signal Processing Society. He is currently serving as an Associate Editor for Coding and Communication Theory for the IEEE Transactions on Communications.

Prof. Xiaohu Tang

Title: On the Placement Delivery Array Design in Centralized Coded Caching Scheme

Abstract: Caching is a promising solution to satisfy the ever-increasing demands for the multi-media traffics. In caching networks, coded caching is a recently proposed technique that achieves significant performance gains over the uncoded caching schemes. In this talk, we firstly review the seminal work of Ali-Niesen caching. Next, we propose a new concept called the placement delivery array (PDA) to characterize the placement issue and the delivery issue with a single array. Moreover, it is sufficient to transform the design of a centralized coded caching scheme to the design of an appropriate PDA. Finally, we present a new construction of PDA, which can significantly decrease the complexity of Ali-Niesen caching scheme but keep almost the same coding gain in some cases.

Bio: Xiaohu Tang received the Ph.D.degree in electronic engineering from the Southwest Jiaotong University,Chengdu, China, in 2001. From 2003 to 2004, he was a research associate in the Department of Electrical and Electronic Engineering, Hong Kong University of Science and Technology. From 2007 to 2008, he was a visiting professor at University of Ulm, Germany. Since 2001, he has been in the School of Information Science and Technology, Southwest Jiaotong University, where he is currently a professor. His research interests include coding theory, network security, distributed storage and information processing for big data. Dr. Tang was the recipient of the National excellent Doctoral Dissertation award in 2003 (China), the Humboldt Research Fellowship in 2007 (Germany), and the Outstanding Young Scientist Award by NSFC in 2013 (China). He serves as Associate Editors for several journals including IEEE TRANSACTIONS ON INFORMATION THEORY and IEICE Transactions on Fundamentals, and served on a number of technical program committees of conferences.

Prof. Jianwei Huang

Title: Communications Network Economics

Abstract: Today’s communication networks are highly complex, carry heterogeneous traffic in diverse environments, and are often owned by multiple profit-making entities. To successfully maintain, optimize, and upgrade such large distributed networks, it is important to design new economic incentive mechanisms as well as develop new technologies. The market deregulation of the telecommunication industry in many countries makes such economic consideration even more urgent, as there are often conflicting goals between the regulators and the commercial operators. I will first illustrate how economics can help us better understand the networking industry reality, predict user behaviors, envision new network services, and provide policy recommendations. Then I will focus on the case study of incentive mechanisms for user-provided networks (UPNs). UPNs is a new communication paradigm, which enables users to improve their communications experiences by exploiting the diverse communication needs and resources of other users. The success of UPNs, however, relies on carefully designed incentive mechanisms that effectively encourage users’ voluntary participations and cooperations. We will introduce a new paradigm of cooperative video streaming based on the concept of UPN, where mobile users crowdsource their Internet connectivities and adaptively choose video downloading sequences and streaming qualities. We will introduce a multi-dimensional auction framework, which effectively incentivizes users to cooperate in a distributed fashion.

Bio: Jianwei Huang is an IEEE Fellow, an IEEE Communications Society Distinguished Lecturer, and a Thomson Reuters Highly Cited Researcher in Computer Science. He is an Associate Professor and Director of the Network Communications and Economics Lab (ncel.ie.cuhk.edu.hk), in the Department of Information Engineering at the Chinese University of Hong Kong. He is also a Shanghai Thousand Talents Plan Expert and a Visiting Professor of Shanghai Jiaotong Univeristy. He received Ph.D. from Northwestern University in 2005 and worked as a Postdoc Research Associate at Princeton University during 2005-2007. His main research interests are in the area of network economics and games, with applications in wireless communications, networking, and smart grid. He is the co-recipient of 8 Best Paper Awards, including IEEE Marconi Prize Paper Award in Wireless Communications in 2011, and Best (Student) Paper Awards from IEEE WiOpt 2015, IEEE WiOpt 2014, IEEE WiOpt 2013, IEEE SmartGridComm 2012, WiCON 2011, IEEE GLOBECOM 2010, and APCC 2009. He has co-authored five books, including the first textbook on “Wireless Network Pricing”. He received the CUHK Young Researcher Award in 2014 and IEEE ComSoc Asia-Pacific Outstanding Young Researcher Award in 2009.

Jianwei Huang has served as an Editor of IEEE Transactions on Cognitive Communications and Networking (2015-), Editor of IEEE Transactions on Wireless Communications (2010-2015), Editor of IEEE Journal on Selected Areas in Communications - Cognitive Radio Series (2011-2014), Editor and Associate Editor-in-Chief of IEEE Communications Society Technology News (2012-2014). He has served as a Guest Editor of IEEE Transactions on Smart Grid special issue on “Big Data Analytics for Grid Modernization” (2016), IEEE Network special issue on “Smart Data Pricing” (2016), IEEE Journal on Selected Areas in Communications special issues on “Game Theory for Networks” (2016), “Economics of Communication Networks and Systems” (2012), and “Game Theory in Communication Systems” (2008), and IEEE Communications Magazine feature topic on “Communications Network Economics” (2012). He Dr. Huang has served as Vice Chair (2015-2016) of IEEE Communications Society Cognitive Network Technical Committee, Chair (2012-2014) and Vice Chair (2010-2012) of IEEE Communications Society Multimedia Communications Technical Committee, a Steering Committee Member of IEEE Transactions on Multimedia (2012-2014) and IEEE International Conference on Multimedia & Expo (2012-2014). He has served as the TPC or Symposium Co-Chair of IEEE WiOpt 2017/2012, IEEE SDP 2016/2015, IEEE ICCC 2015/2012, NetGCoop 2014, IEEE SmartGridComm 2014, IEEE GLOBECOM 2017/2013/2010, IWCMC 2010, and GameNets 2009. He will serve as a General Co-Chair of IEEE WiOpt 2018. He is a frequent TPC member of leading networking conferences such as INFOCOM and MobiHoc. He is the recipient of IEEE ComSoc Multimedia Communications Technical Committee Distinguished Service Award in 2015 and IEEE GLOBECOM Outstanding Service Award in 2010.

Prof. Yang Yang

Title: Open 5G Platform

Abstract: In this talk, we will give an introduction of an open 5G platform, which applies SDN and NFV techniques to realize all the functions of a telecom operator on general CPU/GPU computing platform. New technical challenges and potential applications of this open 5G platform will be fully discussed.

Bio: Yang Yang received the BEng and MEng degrees in Radio Engineering from Southeast University, Nanjing, P. R. China, in 1996 and 1999, respectively; and the PhD degree in Information Engineering from The Chinese University of Hong Kong in 2002.

Dr. Yang Yang is currently a professor with Shanghai Institute of Microsystem and Information Technology (SIMIT), Chinese Academy of Sciences, serving as the Director of CAS Key Laboratory of Wireless Sensor Network and Communication, and the Director of Shanghai Research Center for Wireless Communications (WiCO). He is also an adjunct professor with the School of Information Science and Technology, ShanghaiTech University. Prior to that, he has served the Department of Electronic and Electrical Engineering at University College London (UCL), United Kingdom, as a Senior Lecturer; the Department of Electronic and Computer Engineering at Brunel University, United Kingdom, as a Lecturer; and the Department of Information Engineering at The Chinese University of Hong Kong as an Assistant Professor. His research interests include wireless ad hoc and sensor networks, software defined wireless networks, 5G mobile systems, intelligent transport systems, wireless testbed development and practical experiments.

Dr. Yang Yang has co-edited a book on heterogeneous cellular networks (2013, Cambridge University Press) and co-authored more than 100 technical papers. He has been serving in the organization teams of about 50 international conferences, e.g. a co-chair of Ad-hoc and Sensor Networking Symposium at IEEE ICC’15, a co-chair of Communication and Information System Security Symposium at IEEE Globec’15

Prof. Xiao Ma

Title: A New Simulation Approach to Performance Evaluation of Binary Linear Codes in the Extremely Low Error Rate Region

Abstract: For certain communication systems, including optical fiber transmission systems and magnetic storage systems, it is of importance to design coding schemes with extremely low error rate. Typically, it is extremely time-consuming and even infeasible to evaluate such designs by the traditional Monte Carlo simulations with a limited computational resource due to the difficulty in sampling a sufficient number of rare error events. One can rely on field-programmable gate array (FPGA)-based emulation or make use of importance sampling based on the knowledge of trapping sets. One can also use tight bounds to predict their performance without resorting to computer simulations.

This talk will start with a general bounding framework based on nested Gallager regions. The sphere bound (SB) is then re-derived in the proposed framework. On one hand, the re-derivation reveals the equivalence between the SB proposed by Herzberg and Poltyrev and the SB proposed by Kasami et al.. The former is often cited, as evidenced by the tutorial book by Sason and Shamai where the SB proposed by Herzberg and Poltyrev was reviewed as the only form of the SB. In constrast, the SB proposed by Kasami et al. was rarely cited in the literatures. On the other hand, the re-derivation stimulates us to develop a new simulation approach to evaluate the performance of binary linear codes over additive white Gaussian noise (AWGN) channels. The new approach simulates directly the conditional error probability on the spheres with relatively large radii, where an important fact is invoked that the noise is uniformly distributed over the sphere. Most importantly, the new simulation approach can be used to estimate the number of codewords with minimum Hamming weight, which is critical to evaluate the conditional error probability on those small spheres. The new simulation approach not only matches well with the traditional simulation approach in the high error rate region but also is able to evaluate efficiently the performance in the extremely low error rate region.

Bio: Xiao Ma is a Professor with the Department of Electronics and Communication Engineering, Sun Yat-sen University, Guangzhou, China. He received the Ph.D. degree in communication and information systems from Xidian University, China, in 2000. From 2000 to 2002, he was a Postdoctoral Fellow with Harvard University, Cambridge, MA. From 2002 to 2004, he was a Research Fellow with City University of Hong Kong.

Dr. Ma’s research interests include information theory, channel coding theory and their applications to communication systems and digital recording systems. Dr. Ma is a corecipient, with A. Kavčić and N. Varnica, of the 2005 IEEE Best Paper Award in Signal Processing and Coding for Data Storage. In 2006, Dr. Ma received the Microsoft Professorship Award from Microsoft Research Asia. Dr. Ma is a member of the IEEE.

Dr. Mine Alsan

Title: The Polar Mismatched Capacity: Definition, Characterization, and Lower Bounds

Abstract: Arıkan’s polar coding, is by now a well studied technique that allows achieving the symmetric capacity of binary input memoryless channels with low complexity encoding and decoding, provided that the polar decoding architecture is used and the decoding metric is matched to the true channel. In this talk, we will analyze communication rates that are achievable when the polar coding/decoding architecture is used with the decoder using an incorrect model of the channel. We define the ‘polar mismatched capacity’ as an analogue of the classical mismatched capacity, give an expression for it, and derive lower bounds on it.

Bio: Mine Alsan was born in Turkey in 1984. She is currently working as a Research Fellow in the Department of Electrical and Computer Engineering (ECE) at the National University of Singapore (NUS). She received the B.Sc. degree in electrical and electronics engineering from the Middle East Technical University (METU) in 2007. She received the M.Sc. and the Ph.D. degrees in computer and communication sciences from the École Polytechnique Fédérale de Lausanne (EPFL) in 2010 and 2015, respectively. Her research interests include information theory and machine learning.

Prof. Douglas Zhou

Title: A probability polling state of neuronal systems underlying maximum entropy coding principle

Abstract: How to extract information from exponentially growing recorded neuronal data is a great scientific challenge. In recent experiments, it has been found that the second order maximum entropy model, by using only firing rates and second order correlations of neurons as constraints, can well capture the observed distribution of neuronal firing patterns in many neuronal networks, thus, conferring its great advantage in that the degree of complexity in the analysis of neuronal activity data reduces drastically from $O(2^n)$ to $O(n^2)$, where n is the number of neurons under consideration. In this talk, we address the question of what kind of dynamical states of neuronal networks allows the network to possess a coding scheme dictated by the Maximum Entropy Principle (MEP). For asynchronous neuronal networks, when considering the probability increment of a neuron spiking induced by other neurons, we found a probability polling (p-polling) state that underlies the success of the second order maximum entropy model. We show that this p-polling state can arise in vitro and in vivo. Our theoretical analysis of the p-polling state and its relationship to MEP provides a new perspective to the information coding of neuronal network dynamics in the brain.

Bio: Douglas Zhou obtained his B.S. and Ph.D at Peking University in 2002 and 2007, respectively. From 2007 to 2009, he was a Courant Instructor in Cournat Institute of Mathematical Sciences at New York University. Then, he worked as a Distinguished Research Fellow from 2010 to 2015 and became a professor in 2016 in the Institute of Natural Sciences and School of Mathematical Sciences at Shanghai Jiao Tong University. His main research field is theoretical and computational neuroscience. His works were published in journals such as Proceedings of the National Academy of Sciences, PLoS Computational Biology, and Physical Review letters. For more information, please visit his personal website: http://ins.sjtu.edu.cn/people/zdz/

Prof. Chandra Nair

Title: Communication over a one-sided interference channel

Abstract: This talk examines the achievable rate region for the one-sided (or Z) interference channel both for the discrete channel model and the Gaussian noise setting. In the discrete channel we will see that rates above Han-Kobayashi achievable region is achievable, thus demonstrating the sub-optimality of the Han-Kobayashi region. In the Gaussian noise case, we will show a few results that position the problem in a tantalizing state; and conclude with an information inequality that is shown to be equivalent to the optimality of the Han-Kobayashi region around the Costa corner point.

Bio: Chandra Nair is an Associate Professor with the information engineering department of the Chinese University of Hong Kong. His current research focuses on studying the optimality of certain inner and outer bounds to capacity regions for fundamental problems in multiuser information theory. One of his papers in this area, devising a novel technique for proving Gaussian optimizers, received the 2016 Information Theory Society paper award. Previously, in his dissertation, he gave a proof of the Parisi and Coppersmith-Sorkin conjectures in the Random Assignment Problem; and resolved some conjectures related to Random Energy model approximation of the Number Partition Problem during his post-doctoral years.

Chandra Nair got his Bachelor’s degree from IIT Madras (India) where he was the Philips (India) and Siemens (India) award winner for the best academic performance (EE dept). Subsequently he was a Stanford graduate fellow (00-04) and Microsoft graduate fellow (04-05) during his graduate studies at the EE dept, Stanford university. Later, he became a post-doc (05-07) with the theory group at Microsoft research, Redmond. He joint the faculty of the information engineering department in the Chinese university of Hong Kong during Fall 2007. At the Chinese University of Hong Kong, he is the Programme Director of the undergraduate program on Mathematics and Information Engineering and the Associate director of the Institute of Theoretical Computer Science and Communication.

Dr. Qun Huang

Title: High-Performance Network Analytic for Software Packet Processing

Abstract: Today’s software packet processing platforms have limited support to network measurement. Sketches provide a promising building block for network measurement, as they can monitor every packet with fixed-size memory and incur only bounded errors. However, we find that existing sketch-based solutions suffer from severe performance drops under high traffic load. Although sketches are efficiently designed, applying them in network measurement inevitably incurs heavy computations.

In this talk, we present SketchVisor, a high-performance network measurement platform for software packet processing. SketchVisor augments sketch-based measurement in the data plane with a fast path to provide fast but slightly less accurate processing for the packets that cannot be promptly handled by underlying sketches. We design a recovery algorithm in the control plane that merges data plane results and employs compressive sensing to recover original sketches, so as to provide accurate network-wide measurement. We have built a prototype of SketchVisor and integrated it with Open vSwitch. Ex- tensive evaluations show that SketchVisor achieves high performance and high accuracy under a wide range of network measurement tasks and microbenchmarks.

Bio: Dr. Qun Huang is a researcher at Huawei Future Network Lab. Currently, he leads Network Analytic Project in the lab, which aims to build high-performance and robust architectures for network analytics. Before joining Huawei, he got his Ph.D. degree in 2015 in The Chinese University of Hong Kong. He received his BSc from Peking University in 2011.

Prof. Guangyue Han

Title: On Sampling Theorems

Abstract: This talk concerns sampling theorems for continuous-time Gaussian channels with possible feedback. More specifically, we show that the mutual information of the discrete-time versions of a class of continuous-time Gaussian channels, obtained through sampling in time, will converges to the mutual information of the original Gaussian channel, as the step-sizes of the samplings tend to zero.

Bio: Guangyue Han received the B.S. and M.S. degrees in mathematics from Peking University, China, and the Ph.D. degree in mathematics from the University of Notre Dame, U.S.A. in 1997, 2000 and 2004, respectively. After three years with the department of mathematics at the University of British Columbia, Canada, he joined the department of mathematics at the University of Hong Kong, China in 2007. His main research areas are coding and information theory.

Prof. Richard T. B. Ma

Title: Pay or Perish: On the Economics of Premium Peering

Abstract: As the Internet continues to evolve, traditional peering agreements cannot accommodate the changing market conditions. Premium peering has emerged where access providers (APs) charge content providers (CPs) for premium services beyond best-effort connectivity. Although prioritized peering raises concerns about net neutrality, the U.S. FCC exempted peering agreements from its recent ruling, as it falls short of background in the Internet peeringcontext. In this paper, we consider the premium peering options provided by APs and study whether or not CPs will peer. Based on a novel choice model of complementary services, we characterize the market shares and utilities of the providers under various peering decisions and identify the value of premium peering (VoPP) for the CPs. We find that high-value CPs have peer pressure when low-value CPs peer; however, low-value CPs behave oppositely.The peering decisions of the low- and high-value CPs are substantially influenced by their user stickiness and baseline market shares, respectively, but not vice versa.

Bio: Richard T. B. Ma received the B.Sc. (first class honors) degree in computer science and M.Phil. degree in computer science and engineering from the Chinese University of Hong Kong, Hong Kong, in 2002 and 2004, respectively, and the Ph.D. degree in electrical engineering from Columbia University, New York, NY, USA, in 2010. During his Ph.D. study, he worked as a research intern with the IBM T. J. Watson Research Center, NY, USA, and Telefonica Research, Barcelona, Spain. He is currently an Assistant Professor with the School of Computing, National University of Singapore, Singapore, and a Research Scientist with the Advanced Digital Science Center, University of Illinois at Urbana-Champaign, Urbana, IL, USA. His research interests include distributed systems and network economics.

Prof. Yihong Wu

Title: Coupling-based Converse Methods in Information Theory and Control

Abstract: I will describe new coupling-based methods for proving impossibility results in information theory and stochastic control. With the common theme being bounding information measures by optimal transport (Wasserstein) distances, several applications will be discussed:
(a) optimal memoryless control on Gaussian line network consisting of n noisy Gaussian channels and n power-constrained relays, where we characterize the best non-linear contraction of total variation in Gaussian channels and show that the optimal end-to-end correlation decays as $\Theta(\log\log n/\log n)$, resolving a problem of Lipsa-Martins (2011).
(b) the "missing corner-point" conjecture of Costa (1985) in Gaussian interference channel capacity region, where we settle this conjecture by showing that the output entropy is Lipschitz with respect to the Wasserstein distance and the unknown multi-user interference can be replaced by its iid approximations using couplings given by Talagrand's transportation-information inequality.
and, if time permits,
(c) strong data processing inequalities on Bayesian networks, where we bound the noisiness (in terms of Dobrushin's contraction coefficient) of the network in terms of those of the edges and the network topology.
This is based on joint work with Yury Polyanskiy (MIT).

Bio: Yihong Wu is an assistant professor in the Department of Statistics at Yale University. He received the B.E. degree from Tsinghua University, Beijing, China, in 2006 and the M.A. and Ph.D. degrees from Princeton University, Princeton, NJ, in 2008 and 2011, all in electrical engineering. He was a postdoctoral fellow with the Statistics Department in The Wharton School at the University of Pennsylvania from 2011 to 2012 and an assistant professor in the Department of ECE at the University of Illinois at Urbana-Champaign from 2013 to 2015. His research interests are in the theoretical and algorithmic aspects of high-dimensional statistics and information theory.

Prof. Kenneth Shum

Title: Generic regenerating codes

Abstract: The notion of regenerating codes was initiated by Dimakis et al. in order to address the repair problem in distributed storage systems. By formulating the problem of repair bandwidth minimization as a network coding problem, a lower bound on repair bandwidth can be derived. The basic repair model assumes a fully connected network, and all the data links are homogeneous. To suit different needs of storage systems in practice, various ramifications of the basic repair model are developed. For instance, the storage nodes may be grouped into different racks, so that the inter-rack traffic is much faster than the intra-rack traffic. In this talk, we describe a common framework for the various system models, and give a unifying existence proof of codes achieving the corresponding lower bounds on repair bandwidth. The proof technique relies on generic network codes and concepts from matroid theory.

Bio: Kenneth W. Shum received the B.Eng. degree in information engineering from The Chinese University of Hong Kong in 1993, and the M.S.and Ph.D. degrees in electrical engineering from the University of Southern California in 1995 and 2000, respectively. He is now a research associate professor with the Institute of Network Coding, The Chinese University of Hong Kong. His research interests include information theory and coding for distributed storage systems.

Prof. Sid Jaggi

Title: Communication in the presence of adversarial jamming: The curious case of the erasure channel

Abstract: Alice wishes to communicate with Bob, but the malicious jammer James wishes to stop this communication from occurring. What can she do? In this talk I'll explore recent discoveries/capacity-characterizations in a variety of settings (the role of causality in James' jamming, the role of myopicity in his observations, the interplay between these two, and the effect of rate-limited feedback). I'll focus on one of the simplest non-trivial jamming channels -- the case of a binary input channel, in which James may erase a constant fraction of transmissions -- even here, interesting phenomena occur. Time permitting, I may also briefly describe generalizations to other jamming channels/scenarios.

Bio: B.Tech. ('00), EE, IIT Bombay, MS/Ph.D. ('05) EE, CalTech, Postdoctoral Associate ('06) LIDS, MIT, Currently Associate Professor, Dept. of Information Engineering, The Chinese University of Hong Kong. Research interests: Network coding and network error-correcting algorithms, coding theory, covert communication, sparse recovery.

Prof. Yunghsiang S. Han

Title: Novel FFT over Binary Finite Fields and Its Application to Reed-Solomon Erasure Codes

Abstract: A fundamental issue in algebra is to reduce the computational complexities of arithmetic operations over polynomials. Many fast polynomial-related algorithms, such as encoding/decoding of Reed-Solomon codes, are based on fast Fourier transforms (FFT). However, it is algorithmically harder as the traditional fast Fourier transform (FFT) cannot be applied directly over characteristic-2 finite fields. To the best of our knowledge, no existing algorithm for characteristic-2 finite field FFT/polynomial multiplication has provably achieved O(hlog₂(h)) operations. In this talk, we present a new basis of polynomial over finite fields of characteristic-2 and then apply it to the encoding/decoding of Reed-Solomon erasure codes. The proposed polynomial basis allows that h-point polynomial evaluation can be computed in O(hlog₂(h)) finite field operations with small leading constant. As compared with the canonical polynomial basis, the proposed basis improves the arithmetic complexity of addition, multiplication, and the determination of polynomial degree from O(hlog₂(h)log₂ log₂(h)) to O(hlog₂(h)). Based on this basis, we then develop the encoding and erasure decoding algorithms for the (n = 2^r; k) Reed-Solomon codes. Thanks to the efficiency of transform based on the polynomial basis, the encoding can be completed in O(nlog₂(k)) finite field operations, and the erasure decoding in O(nlog₂(n)) finite field operations. To the best of our knowledge, this is the first approach supporting Reed-Solomon erasure codes over characteristic-2 finite fields while achieving a complexity of O(nlog₂(n)), in both additive and multiplicative complexities. As the complexity of leading factor is small, the algorithms are advantageous in practical applications.

This work was presented at the 55th Annual Symposium on Foundations of Computer Science (FOCS 2014).

Bio: Yunghsiang S. Han received B.Sc. and M.Sc. degrees in electrical engineering from the National Tsing Hua University, Taiwan, in 1984 and 1986, respectively, and a Ph.D. degree from the School of Computer and Information Science, Syracuse University, NY, in 1993. He was with 1 Hua Fan College of Humanities and Technology, National Chi Nan University, and National Taipei University, Taiwan. From August 2010, he is with the Department of Electrical Engineering at National Taiwan University of Science and Technology as a chair professor.

Dr. Han's research interests are in error-control coding, wireless net- works, and security. Dr. Han has conducting state-of-the-art research in the area of decoding error-correcting codes for more than twenty years. He first developed a sequential-type algorithm based on Algorithm A* from artificial intelligence. At the time, this algorithm drew a lot of attention since it was the most efficient maximum-likelihood decoding algorithm for binary linear block codes. Dr. Han has also successfully applied coding theory in the area of wireless sensor networks. He has published several highly cited works on wireless sensor networks such as random key pre-distribution schemes. He also serves as the editors of several international journals.

Dr. Han was the winner of the Syracuse University Doctoral Prize in 1994 and a Fellow of IEEE. One of his papers won the prestigious 2013 ACM CCS Test-of-Time Award in cybersecurity to recognize its significant impact on the security area over ten years.

Prof. Mehul Motani

Title: Coding for Constrained Communication Systems

Abstract: We motivate the study of codes for constrained communication systems. Constrained codes are useful for applications such as power line communications, low cost authentication systems, and joint energy and information transfer. We analyze various subblock code constraints, in which each codeword is partitioned into smaller subblocks and each subblock is constrained in a particular manner. In particular, we are interested in the following: (i) channel capacity and error exponents for discrete memoryless channels for these subblock constrained codes, (ii) Code sizes and asymptotic rates of these subblock constrained codes, and (iii) Practical subblock constrained codes via concatenation.

Bio: Mehul Motani received the B.E. degree from Cooper Union, New York, NY, the M.S. degree from Syracuse University, Syracuse, NY, and the Ph.D. degree from Cornell University, Ithaca, NY, all in Electrical and Computer Engineering.

Dr. Motani is currently an Associate Professor in the Electrical and Computer Engineering Department at the National University of Singapore (NUS) and a Visiting Research Collaborator at Princeton University. Previously, he was a Visiting Fellow at Princeton University. He was also a Research Scientist at the Institute for Infocomm Research in Singapore, for three years, and a Systems Engineer at Lockheed Martin in Syracuse, NY for over four years. His research interests are broadly in the area of wireless networks. Recently he has been working on research problems which sit at the boundary of information theory, networking, and communications, with applications to mobile computing, underwater communications, sustainable development and societal networks.

Dr. Motani was the recipient of the Intel Foundation Fellowship for his Ph.D. research, the NUS Annual Teaching Excellence Award, the NUS Faculty of Engineering Innovative Teaching Award, and the NUS Faculty of Engineering Teaching Honours List Award. He is a senior member of the IEEE and has served as the Secretary of the IEEE Information Theory Society Board of Governors. He has served as an Associate Editor for both the IEEE Transactions on Information Theory and the IEEE Transactions on Communications.

Prof. Ayfer Ozgur Aydin

Title: The Geometry of the Relay Channel

Abstract: Formulating the problem of determining the communication capacity of channels as a problem in high-dimensional geometry is one of Shannon’s most important insights that has led to the conception of information theory. In his classical paper “Communication in the presence of noise”, 1949, Shannon develops a geometric representation of any point-to-point communication system and provides a geometric proof of the coding theorem for the AWGN channel where the converse is based on a sphere-packing argument in high-dimensional space. We show that a similar geometric approach can be used to prove converses for network communication problems. In particular, we solve a long-standing open problem posed by Cover and named “The Capacity of the Relay Channel,” in Open Problems in Communication and Computation, Springer-Verlag, 1987. The key step in our proof is a strengthening of the isoperimetric inequality on a high-dimensional sphere, which we use to develop a packing argument on a spherical cap, similar to Shannon's original approach. We discuss the promise of this geometric approach for solving other open problems in network information theory.

Bio: Ayfer Ozgur received her B.Sc. degrees in electrical engineering and physics from Middle East Technical University, Turkey, in 2001 and the M.Sc. degree in communications from the same university in 2004. From 2001 to 2004, she worked as hardware engineer for the Defense Industries Development Institute in Turkey. She received her Ph.D. degree in 2009 from the Information Processing Group at EPFL, Switzerland. In 2010 and 2011, she was a post-doctoral scholar with the Algorithmic Research in Network Information Group at EPFL. She is currently an Assistant Professor in the Electrical Engineering Department at Stanford University. Her research interests include network communications, wireless systems, and information and coding theory. Dr. Ozgur received the EPFL Best Ph.D. Thesis Award in 2010 and a NSF CAREER award in 2013.

Prof. Ziyu Shao

Title: Complex Engineered Networks: An Optimization Perspective

Abstract: Complex engineered networks are everywhere: data center networks, Internet, wireless communication systems, power grids, transportation networks, and more. These networks have evolved into complex systems with behaviors and characteristics that are beyond the characterizations and predictions possible by the traditional modeling, analysis and design approaches. In this talk, we will introduce the recent efforts to understand complex engineered networks from an optimization perspective. By several case studies, I will show that a simple yet powerful network optimization framework can not only provide fresh perspective to existing solutions, but also help us generate new algorithms in various domains with provable performance.

Bio: Ziyu Shao received B.S. and M.S. degrees in EE from Peking University, and the Ph.D. degree in information engineering from The Chinese University of Hong Kong, in 2001, 2004 and 2010, respectively. During 2010-2013, he was a Postdoctoral Fellow at The Chinese University of Hong Kong and a visiting scholar at Princeton University. He has been an Assistant Professor with the School of Information Science and Technology at ShanghaiTech University, Shanghai, China since January 2014. His research interests include stochastic modeling and analysis, learning and optimization, data center networking, wireless networking and network coding.

Prof. Wenyi Zhang

Title: On many-access over OR channel

Abstract: New demand in future wireless networks requires the capability of efficiently handling concurrent connection requests of a massive number of potential users, each with possibly low duty cycle and short message to send. Conventional multiuser information theory focuses on the regime of a fixed number of users and long messages, and thus usually falls short of characterizing the behavior of communication systems under such a “many-access” scenario. In this talk, we present our preliminary results of many-access over an OR channel, which implements an OR logic upon all the active users’ channel inputs. Our results include capacity analysis and coding schemes.

Bio: Wenyi Zhang is with the faculty of the University of Science and Technology of China. He graduated from the University of Notre Dame (Ph.D. in EE) and Tsinghua University (B.E. in Automation). His current research interest focuses on information transmission under nonlinear transceiver distortion, large-scale multiaccessing, statistical inference over networks, and sequential decision theory.

Prof. Longbo Huang

Title: Receding Learning-aided Control in Stochastic Networks

Abstract: We develop the Receding Learning-aided Control algorithm (RLC) for solving optimization problems in general stochastic networks with potentially non-stationary system dynamics. RLC is a low-complexity online algorithm that requires zero a-priori statistical knowledge. It has three main functionalities. First, it detects changes of the underlying distribution of system dynamics via receding sampling. Then, it carefully selects the sampled information and estimates a Lagrange multiplier of an underlying optimization problem via dual-learning. Lastly, it incorporates the multiplier into an online system controller via drift-augmentation. We show that RLC achieves near-optimal utility-delay tradeoffs for stationary systems, while ensuring an efficient distribution-change detection and a fast convergence speed when applied to non-stationary networks. The results in this work provide a general framework for designing joint detection-learning-control algorithms and provide new understanding about the role-of-information and the power-of-online-learning in network control.

Bio: Dr. Longbo Huang is an assistant professor at the Institute for Interdisciplinary Information Sciences (IIIS) at Tsinghua University, Beijing, China. He received his Ph.D. in EE from the University of Southern California in August 2011, and then worked as a postdoctoral researcher in the EECS dept. at University of California at Berkeley from July 2011 to August 2012.

Dr. Huang has been a visiting scholar at the LIDS lab at MIT and at the EECS department at UC Berkeley, and a visiting professor at the Chinese University of Hong Kong, Bell-labs France and Microsoft Research Asia. He was also a visiting scientist at the Simons Institute for the Theory of Computing at UC Berkeley in Fall 2016. Dr. Huang was selected into China’s Youth 1000-talent program in 2013, and received the outstanding teaching award from Tsinghua university in 2014.

Dr. Huang’s current research interests are in the areas of online learning, network optimization, online algorithm design, and sharing economy.

Prof. Thomas Courtade

Title: Entropy and Convolution Inequalities

Abstract: One of the most fundamental properties of entropy is its behavior under convolution, which is succinctly described by Shannon's entropy power inequality (EPI). Beyond its applications to coding theorems, the EPI has a remarkably rich set of functional consequences: Gross' logarithmic Sobolev inequality, the Gaussian concentration inequality for Lipschitz functions, Talagrand's information-transportation inequality and the Gaussian Poincaré inequality may all be viewed as consequences of the EPI. In this talk, I will present recent refinements of the EPI and discuss their various implications, including new convolution inequalities.

Bio: Thomas Courtade is an Assistant Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. Prior to joining UC Berkeley in 2014, he was a postdoctoral fellow supported by the NSF Center for Science of Information. He received his Ph.D. and M.S. degrees from UCLA in 2012 and 2008, respectively, and he graduated summa cum laude with a B.Sc. in Electrical Engineering from Michigan Technological University in 2007. His honors include a Hellman Fellowship, a Distinguished Ph.D. Dissertation award and an Excellence in Teaching award from the UCLA Department of Electrical Engineering, and a Best Student Paper Award for the 2012 International Symposium on Information Theory.

Prof. Changho Suh

Title: High-dimensional Codes for Distributed Computing

Abstract: Distributed computing is the basic workhorse of large-scale computing systems such as Google’s data center and Microsoft Azure. One challenge that arises in this context is: Computing time does not scale well with the number n of distributed computing units due to a variety of system noises such as stragglers and system failure. A recent effort has been made towards overcoming the challenge: A coding-theoretic approach is introduced to speed up computation time in an order of magnitude. Specifically for matrix-vector multiplication, it has been shown that an erasure code can provide a logn factor gain in speed over the uncoded computation. However, it is not clear whether such approach is effective also for more demanding tasks such as very large matrix-matrix multiplication.

In this talk, we first describe when and why the existing coding approach fails to achieve the promising gain for large matrix-matrix multiplication. To address the challenge, we invoke Shannon’s block coding idea. Our approach delicately splits high-dimensional data to low-dimensional data (e.g., matrices to vectors) and then applies a block code across the low-dimensional components fragmented, thereby achieving the maximal coding gain. In particular, for matrix-matrix multiplication, we employ a product code – comprising two erasure codes - to achieve the fundamental limit of computing time that scales like 1/n. As a consequence, we show that our code offers a logn factor improvement over the uncoded and prior coding approaches. Moreover, our coding scheme requires almost the same communication overhead relative to the uncoded computation, as well as relies on an efficient peeling decoder.

This is joint work with Dr. Kangwook Lee (@KAIST) and Prof. Kannan Ramchandran (@UC-Berkeley).

Bio: Changho Suh is an Ewon Associate Professor in the School of Electrical Engineering at Korea Advanced Institute of Science and Technology (KAIST). He received the B.S. and M.S. degrees in Electrical Engineering from KAIST in 2000 and 2002 respectively, and the Ph.D. degree in Electrical Engineering and Computer Sciences from UC-Berkeley in 2011. From 2011 to 2012, he was a postdoctoral associate at the Research Laboratory of Electronics in MIT. From 2002 to 2006, he had been with the Telecommunication R&D Center, Samsung Electronics.

Dr. Suh received the 2015 IEIE Haedong Young Engineer Award, a 2015 Bell Labs Prize finalist, the 2013 IEEE Communications Society Stephen O. Rice Prize, the 2011 David J. Sakrison Memorial Prize (top research award in the UC-Berkeley EECS Department), and the 2009 IEEE ISIT Best Student Paper Award.

Prof. Fan Cheng

Title: Gaussian Complete Monotonicity Conjecture -- When Shannon Meets Gauss

Abstract: A function $g(s)$ is completely monotone (CM) in $s$ if the signs of all its consecutive derivatives are alternating in "$+$" and "$-$". e.g., $g(s) = 1/s$. Let $X$ and $Z$ be mutually independent random variables and $Z$ is the Gaussian distributed. Gaussian Complete Monotonicity Conjecture states that Fisher Information $I(X+\sqrt{t}Z)$ is CM in $t > 0$. Since $I(X+\sqrt{t}Z)$ is the first order derivative of $h(X+\sqrt{t}Z)$, the differential entropy of $X+\sqrt{t}Z$, the conjecture will reveal a profound secret about Gaussian distribution in the language of information.

In the literature, the Gaussian complete monotonicity conjecture was first studied in a 1966 paper by H. P. McKean in mathematical physics. However, the conjecture remained open and unknown to other society and was nearly forgotten in the elapsed decades. In our 2015 paper [CG15], we independently introduced the same conjecture and made a significant progress on proving the conjecture with more explicit discoveries. In this talk, we will also introduce the connection with Boltzmann equation, Shannon Entropy Power Inequality and completely monotone functions.

Bio: Dr. Fan Cheng is a tenure track research professor in the department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China. He received the B.E degree from Shanghai Jiao Tong University in 2007 and the PhD degree from The Chinese University of Hong Kong in 2012, respectively. His research interests are focused on the mathematical theory of Engineering problems, including information theory, networking, security, and statistical learning theory.

[CG15] F. Cheng and Y. Geng, "Higher Order Derivatives in Costa’s Entropy Power Inequality,'' IEEE Trans. Inform. Theory, vol. 61, no. 11, pp. 5892-5905, Nov. 2015.