Laura Haas (NAE member, ACM/AAAS Fellow)
Professor and Dean, University of Massachusetts Amherst, USA
Keynote Talk Title: Computing for the Common Good
Time: 8:30-9:30, Tuesday, February 19, 2019
Abstract: We are poised on the brink of a new era of computing, one in which computer systems will be able to handle many of the tasks that only humans can do today. The new systems will feed on data and leverage a diverse set of analytic tools and strategies, constantly learning. They will be able to partner with people to improve our lives and enhance our ability to solve complex problems. But there are risks that arise with this new technology: systems can exhibit bias, leading to unfair results, or they may behave oddly and be hard to explain. To counteract these issues, researchers at the University of Massachusetts Amherst are embracing a vision of “Computing for the Common Good”: computing that not only may be used for good, but that is also, intrinsically, good – fair, accessible, explainable, trustworthy, effective and efficient. In this talk, I will discuss this vision, illustrate it in action with examples of ongoing research and its application, and end with a few thoughts on open research challenges.
Dr. Laura Haas joined the University of Massachusetts Amherst in August 2017 as Dean of the College of Information and Computer Sciences, after a long career at IBM, where she was accorded the title IBM Fellow in recognition of her impact. At the time of her retirement from IBM, she was Director of IBM Research’s Accelerated Discovery Lab (2011-2017), after serving as Director of Computer Science at IBM’s Almaden Research Center from 2005 to 2011. She had worldwide responsibility for IBM Research’s exploratory science program from 2009 through 2013. From 2001-2005, she led the Information Integration Solutions architecture and development teams in IBM's Software Group. Previously, Dr. Haas was a research staff member and manager at Almaden. She is best known for her work on the Starburst query processor, from which DB2 LUW was developed, on Garlic, a system which allowed integration of heterogeneous data sources, and on Clio, the first semi-automatic tool for heterogeneous schema mapping. She has received several IBM awards for Outstanding Innovation and Technical Achievement, an IBM Corporate Award for information integration technology, the Anita Borg Institute Technical Leadership Award, and the ACM SIGMOD Edgar F. Codd Innovation Award. Dr. Haas was Vice President of the VLDB Endowment Board of Trustees from 2004-2009 and served on the board of the Computing Research Association from 2007-2016 (vice chair 2009-2015); she currently serves on the National Academies Computer Science and Telecommunications Board (2013-2019). She is an ACM Fellow, a member of the National Academy of Engineering, and a Fellow of the American Academy of Arts and Sciences.
Ali H. Sayed (NAE member, IEEE/AAAS Fellow)
Dean and Professor, EPFL, Switzerland
Keynote Talk Title: Diffusion of Information over Graphs
Time: 8:30-9:30, Monday, February 18, 2019
Information flow over graphs is a topic of significant relevance, especially in modern days with the recurrent propagation of ``fake'' information over social networks. This issue is gaining prominence with the proliferation of online platforms, which facilitate communications and the exchange of opinions among members. In order to promote the diffusion of reliable information, it is important to understand which aspects of the graph topology favor the spread of misinformation, and what strategies can be used to control opinions, sow discord, or resist manipulation. This presentation provides an overview of research results on distributed information flow over weak graphs where the flow of information is asymmetric. This scenario is common over social networks. For example, it is not unusual for some influential agents (such as celebrities) to have a large number of followers, while the influential agent may not be following most of them. A similar effect arises when social networks operate in the presence of stubborn agents, which insist on their opinion regardless of the evidence provided by observations or by neighboring agents. It turns out that weak graphs influence the evolution of the agents’ beliefs in an interesting manner and facilitate the spread of false information. While agents are able to learn the global truth from interactions over strong graphs, where there is a path between any two agents, anomalies arise over weak graphs where certain agents can control the opinion of other agents. This phenomenon permits the flow of misinformation and can be used to sow discord and confusion. In particular, (a) agents can be made to arrive at incorrect inference decisions (a form of belief control); (b) they can be made to disagree among themselves (a form of social discord); and (c) and they can be made to continually change their beliefs about the truth (a form of confused learning). For instance, some agents may be driven to believe erroneously that “it is raining” even though they may be observing “sunny conditions.” This presentation examines these patterns of behavior over multi-agent networks and illustrates the results with examples and simulations.
Ali H. Sayed is Dean of Engineering at EPFL, Switzerland. He has also served as distinguished professor and former chairman of electrical engineering at UCLA, Los Angeles. He is recognized as a Highly Cited Researcher by Thomson Reuters and is also a member of the US National Academy of Engineering. An author of over 530 scholarly publications and six books, his research involves several areas including adaptation and learning, data and network sciences, and multi-agent systems. Dr. Sayed has received several awards including the 2015 Education Award from the IEEE Signal Processing Society, the 2014 Athanasios Papoulis Award from the European Association for Signal Processing, the 2013 Meritorious Service Award, and the 2012 Technical Achievement Award from the IEEE Signal Processing Society. He has also received the 2005 Terman Award from the American Society for Engineering Education, the 2003 Kuwait Prize, and the 1996 IEEE Donald G. Fink Prize. His articles received several Best Paper Awards from the IEEE. He is a Fellow of IEEE and the American Association for the Advancement of Science (AAAS). He is serving as President of the IEEE Signal Processing Society.
Raj Jain (IEEE Life Fellow, ACM/AAAS Fellow)
Professor, Washington University in St. Louis., USA
Keynote Talk Title: Trends and Issues in Softwarization of Networks: What's In What's Out?
Time: 8:30-9:30, Wednesday, February 20, 2019
We begin with developments that led to virtualization, network function virtualization, and disaggregation. How software-defined networking definition has changed due to disaggregation. The trend of DevOps and the slowness of the standardization activities has led the networking industry towards open source software and hardware. Open sourcing has now become the fastest way to introduce new research, idea, concept or technology. All these trends and their impact on networking research along several related myths and issues will be discussed in this talk.
Raj Jain is the Barbara J. and Jerome R. Cox, Jr., Professor of Computer Science and Engineering at Washington University in St. Louis. Previously, he was one of the Cofounders of Nayna Networks, Inc - a next-generation telecommunications systems company in San Jose, CA. Dr. Jain has a Ph.D. in applied mathematics and computer science from Harvard University. He is a Fellow of IEEE, ACM, AAAS, and Academy of Science St. Louis. He is the recipient of 2017 ACM SIGCOMM award, 2015 A. A. Michelson Award from Computer Measurement Group and numerous other awards. With 27,000+ citations according to Google Scholar, he is one of the most cited authors in computer science. Further information is at http://www.cse.wustl.edu/~jain/
Lizhong Zheng (IEEE Fellow)
Professor, Massachusetts Institute of Technology
Keynote Talk Title: Connecting Data with Domain Knowledge in Neural Networks
Time: 8:30-9:30, Thursday, February 21, 2019
Part of the power of neural networks comes from the fact that it is a very generic, almost "blind", tool to extract useful information directly from data. Unlike more conventional data analysis approaches, it does not assume any knowledge of the statistical model, the structure, relation or constraints in the data, but tries to use a universal network structure to learn and represent all types of models. On the other hand, if we do have some of such knowledge of the model, such as in communication systems and almost all other engineering systems, in principle we should be able to make the inference algorithms more efficiently by taking advantage of the domain knowledge. It is however not clear how the domain knowledge should be used in deep learning in general. This talk tries to address this issue.
Conceptually, we need to identify what happens inside a neural network during the learning process, to find out what statistical quantities are being calculated and how are they stored inside a network. To that end, we formulate a new problem called the "universal feature selection" problem, where we need to select from the high dimensional data a low dimensional feature that can be used to solve, not one, but a family of inference problems. We solve this problem and show that 1) the solution is closely related to a number of concepts in information theory and statistics such as the HGR correlation and common information, and 2) a number of learning algorithms, PCA, CCA, Matrix Factorization, and Neural Networks, implicitly solve the same problem. We then demonstrate how such theoretical understanding of neural networks can help us to establish a performance limit and design network parameters more systematically; and to include specific domain knowledge in the design of new network structures.
Lizhong Zheng received the B.S and M.S. degrees, in 1994 and 1997 respectively, from the Department of Electronic Engineering, Tsinghua University, China, and the Ph.D. degree, in 2002, from the Department of Electrical Engineering and Computer Sciences, University of California, Berkeley. Since 2002, he has been working at MIT, where he is currently a professor of Electrical Engineering. His research interests include information theory, statistical inference, communications, and networks theory. He received Eli Jury award from UC Berkeley in 2002, IEEE Information Theory Society Paper Award in 2003, and NSF CAREER award in 2004, and the AFOSR Young Investigator Award in 2007. He served as an associate editor for IEEE Transactions on Information Theory, and the general co-chair for the IEEE International Symposium on Information Theory in 2012. He is an IEEE fellow.
Victor Bahl (IEEE/ACM/AAAS Fellow)
Distinguished Scientist and Director, Microsoft, USA
Plenary Forum Title: Embracing the Edge: a paradigm shift in cloud computing
Time: 10:00-12:00, Wednesday, February 20, 2019
Edge computing is a natural evolution of cloud computing, where compute resources, ranging from a credit-card size computer to micro data centers, are placed closer to data (information) generation sources. Application and system developers use these resources to enable a new class of latency- and bandwidth-sensitive applications that are not realizable with current mega-scale cloud computing architectures. Edge computing is in the center of the Internet-of-things revolution. Industries, ranging from manufacturing to healthcare, retail to space-exploration are infusing edge-based information technologies into their day-to-day processes and tasks. They are developing real-time control systems that use sensors and actuators along with machine learning and artificial intelligence to create new functions, improve efficiency and reduce cost.
In this talk, I will explore this exciting new computing paradigm. I will discuss the history and evolution of the intelligent edge and one of its “killer” application. Specifically, I will describe our edge-based, hybrid cloud live video analytics system called Rocket along with a case study of a smart-city traffic management system that we have deployed. With Rocket, we are moving forward aggressively towards the democratization of edge-based video analytics. Time permitting, I will also discuss how we are addressing some of the challenges facing the large-scale adoption of edge computing and future business impact.
Victor Bahl is a distinguished scientist and director of mobility & networking research in Microsoft. He serves on the Microsoft Research Redmond Lab leadership team managing over 200 researchers, engineers, and staff. He advises Microsoft’s CEO and his senior leadership team on strategy and long-term vision related to networked systems, cloud computing, data center infrastructure, mobile computing, and wireless systems. Dr. Bahl has published over 125 papers with over 45,000 citations. He has been granted 150+ patents and delivered over 45 keynotes and plenaries. For his seminal work in wireless systems and broadband access he has received two lifetime achievement awards including the IEEE Koji Kobayashi Computers and Communications Award, the ACM SIGMOBILE Outstanding Contributions Award. He has also been honored with two United States FCC awards, two national transportation awards, two test-of-time awards, three best paper awards, two distinguished alumni awards, a distinguished service award, and a IEEE outstanding leadership award. Under his direction, his group has had game-changing impact on world’s spectrum regulations and policies, and on Microsoft’s cloud computing infrastructures including its data center networks, wide-area networks, edge computing and live video analytics. Dr. Bahl is the founder of ACM SIGMOBILE, ACM MobiSys, ACM GetMobile and several other important conferences. With his wife, he co-founded Computing For All, a non-profit dedicated to increasing and enhancing computer science education for students of all ages and from all backgrounds. Dr. Bahl is a Fellow of the ACM, IEEE, and AAAS.
Elza Erkip (IEEE Fellow)
Institute Professor, New York University, USA.
Plenary Talk Title: An information theoretic perspective on web privacy
Time: 14:30-15:30, Tuesday, February 19, 2019
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 talk 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.
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 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.
Monisha Ghosh (IEEE Fellow)
NSF Director and Research Professor, University of Chicago
Plenary Talk Title: "Fair" coexistence in unlicensed spectrum: past, present and future
Time: 14:30-15:30, Wednesday, February 20, 2019
The discussion around "fair" coexistence in the unlicensed bands has mostly centered around Wi-Fi and LTE and focused on throughput. However, there are additional criteria such as association latency that are equally important in the discussion around fairness but less well studied. In this talk we will present recent results on coexistence between dissimilar systems in the unlicensed bands today and suggestions for how coexistence can be improved in the future. We will also discuss the recent FCC Notice for Proposed Rulemaking (NPRM) that will make available 1.2 GHz of spectrum in the 6 GHz band for unlicensed use. This new allocation presents some unique challenges with respect to incumbent protection and sharing mechanisms that will spur the next wave of research in spectrum sharing and coexistence.
Dr. Monisha Ghosh joined NSF as a rotating Program Director in September 2017, in the Computer and Network System (CNS) division within the Directorate of Computer & Information Science and Engineering (CISE). She manages wireless networking research within the Networking Technologies and Systems (NeTS) program. Dr. Ghosh is also a Research Professor at the University of Chicago, with a joint appointment at the Argonne National Laboratories, where she conducts research on wireless technologies for the IoT, 5G cellular, next generation Wi-Fi systems, coexistence and machine learning for predictive oncology. Prior to joining the University of Chicago in September 2015, she worked at Interdigital, Philips Research and Bell Laboratories, on various wireless systems such as the HDTV broadcast standard, cable standardization and on cognitive radio for the TV White Spaces. She has been an active contributor to many industry standards and was recognized with a Certificate of Appreciation for her outstanding contributions to IEEE 802.22. She is a Fellow of the IEEE.
She received her Ph.D. in Electrical Engineering from the University of Southern California in 1991, and her B. Tech from the Indian Institute of Technology, Kharagpur (India) in 1986.
Tom Hou (IEEE Fellow)
Bradley Distinguished Professor, Virginia Tech, USA
Plenary Talk Title: Real-Time Resource Allocation for 5G NR
Time: 13:30-14:30, Wednesday, February 20, 2019
As the next-generation cellular communication technology, 5G New Radio (NR) aims to cover a wide range of service cases, including broadband human-oriented communications, time-sensitive applications with ultra-low latency, and massive connectivity for Internet of Things. With its broad range of operating frequencies, the channel coherence time for NR varies greatly. To address such needs, a number of different OFDM numerologies are defined for NR, allowing a wide range of frequency and time granularities for data transmission. Under this numerology, it is necessary to perform scheduling with a time resolution as small as ∼100 µs. This requirement poses a new challenge that does not exist in LTE and cannot be supported by any existing LTE schedulers. In this talk, I will present the design of GPF – a GPU-based proportional fair (PF) scheduler that can meet the ∼100 µs time requirement. The key ideas in the design include decomposing the scheduling problem into a large number of small and independent sub-problems and selecting a subset of sub-problems from the most promising search space to fit into a GPU platform. By implementing GPF on an off-the-shelf Nvidia Quadro P6000 GPU, we show that GPF is able to achieve near-optimal performance while meeting the ∼100 µs time requirement. GPF represents the first successful design of a GPU-based PF scheduler that can meet the new time requirement in 5G NR.
Tom Hou is the Bradley Distinguished Professor of Electrical and Computer Engineering at Virginia Tech, USA. He received his Ph.D. degree from NYU Tandon School of Engineering (formerly Polytechnic University) in 1998. His current research focuses on developing innovative solutions to complex science and engineering problems arising from wireless and mobile networks. He is particularly interested in exploring new performance limits at the network layer by exploiting advances at the physical layer. In recent years, he has been actively working on cross-layer optimization problems for cognitive radio wireless networks, cooperative communications, MIMO-based networks and energy related problems. He is also interested in wireless security. Prof. Hou was named an IEEE Fellow for contributions to modeling and optimization of wireless networks. He has published two textbooks: Cognitive Radio Communications and Networks: Principles and Practices (Academic Press/Elsevier, 2009) and Applied Optimization Methods for Wireless Networks (Cambridge University Press, 2014). The first book has been selected as one of the Best Readings on Cognitive Radio by the IEEE Communications Society. Prof. Hou’s research was recognized by five best paper awards from the IEEE and two paper awards from the ACM. He holds five U.S. patents.
Prof. Hou is a prominent leader in the research community. He was an Area Editor of IEEE Transaction on Wireless Communications (Wireless Networking area), and an Editor of IEEE Transactions on Mobile Computing, IEEE Journal on Selected Areas in Communications – Cognitive Radio Series, and IEEE Wireless Communications. Currently, he is an Editor of IEEE/ACM Transactions on Networking and ACM Transactions on Sensor Networks. He is the Steering Committee Chair of IEEE INFOCOM conference – the largest and top ranked conference in networking. He is a member of the Board of Governors as well as a Distinguished Lecturer of the IEEE Communications Society.
Yuji Inou (IEEE Life Fellow)
Chairman of Toyota Info Technology Center, Toyota, Japan
Plenary Forum Title: AI/ML - A New Paradigm for Communication Networks
Time: 10:00-12:00, Wednesday, February 20, 2019
Yuji Inoue was born in 1948 in Fukuoka, Japan. He received the B.E., M.E. and Ph. D degrees from Kyushu University, Fukuoka, Japan, in 1971, 1973 and 1986, respectively, and was made an Honorary Professor of the Mongolian Technical University in 1999.
He joined NTT Laboratories in 1973. He was first engaged in the development of digital network equipment and systems, and then in the standardization of ISDN (Integrated Services Digital Network), SDH (Synchronous Digital Hierarchy) etc in ITU-T, then he was the chairperson of technical committee in TINA-C, Telecommunication Information Networking Architecture Consortium. In 1997, he lead a global business development division when NTT was de-regulated to the global business. He moved to NTT Data Corporation to head its R&D as a board member in 2000. He moved back to NTT Shareholding Company as its board member and CTO to direct NTT group’s R&D with 6000 researchers and engineers.
In 2006, he was the candidate for the Director of ITU-T. Based on this experience, he joined TTC, The Telecommunication Technology Committee, in 2007 as the President and CEO.
In 2010, he moved to Toyota Info Technology Center Co., Ltd. as the Chairman of the Board, where he has been supporting Toyota Motor Company as one of the leading edge Connected Car Services Providers. He is now serving Toyota ITC as the Adviser.
He is a life-time fellow of IEEE, and a Honorable Professor of Mongolian Science and Technology University. He received many awards such as Japanese Ministers Awards. He wrote and edited many technical books.
Head of Service Oriented Network Research Center, Fujitsu, Japan
Plenary Forum Title: Data oriented communication Network : Catalyst for Digital Transformation
Time: 10:00-12:00, Wednesday, February 20, 2019
For accelerating digital transformation, ICT system is expected to be adaptively optimize for business environment. As for network, SDN enables automation based on programed logic that predefined. But it is not enough for optimum as speed and customization point of view. System requires logic itself to be adaptively tuning for environment and AI /ML is expected to utilize. As a result, network is going to “autonomous” from “automation”. Another key point in digital transformation is the Data. For utilize the Data, network role will change from just “transfer the data”, to “deliver the exact data from data owner to data consumer” based on data usage. Network will evolve from connecting computer to connecting data and functions. I introduce activities for network operation utilize AI and also introduce data oriented network utilize blockchain
Mr. Sekiya Motoyoshi joined Fujitsu Limited 1990 and has been engaged in research and development of optical communication system, includes Optical module, WDM system, Network software etc. He joined Fujitsu labs of America in 2010 and leading the research group in photonic networks, optical transmission system and SDN. Currently, he is Head of Service Oriented Network Research Center Fujitsu Labs Limited. He has served as technical committee member of OFC/NFOEC, Globecom workshop SDN for Optics, ICCVE, AICT, ONDM etc.. He is an inventor of more than 70 patents.
Pramod K. Varshney (IEEE Fellow)
Distinguished Professor, Syracuse University
Plenary Talk Title: Information Fusion: Theory and Applications
Time: 13:30-14:30, Tuesday, February 19, 2019
Information fusion refers to the acquisition, processing and synergistic combination of information gathered by various knowledge sources and sensors to provide a better understanding of a phenomenon. This fascinating field has evolved over the past three decades. Information fusion concepts are being applied to a wide variety of fields such as military command and control, robotics, image processing, air traffic control, medical diagnostics, pattern recognition, environmental monitoring, IoT and smart cities. This talk will present an overview of the field, present some recent research results and illustrate its utility by means of some examples.
Pramod K. Varshney was born in Allahabad, India, in 1952. He received the B.S. degree in electrical engineering and computer science (with highest honors), and the M.S. and Ph.D. degrees in electrical engineering from the University of Illinois at Urbana-Champaign in 1972, 1974, and 1976 respectively.
Since 1976 he has been with Syracuse University, Syracuse, NY where he is currently a Distinguished Professor of Electrical Engineering and Computer Science and the Director of CASE: Center for Advanced Systems and Engineering. He served as the Associate Chair of the department during 1993-96. He is also an Adjunct Professor of Radiology at Upstate Medical University in Syracuse, NY. His current research interests are in distributed sensor networks and data fusion, detection and estimation theory, wireless communications, physical layer security, image processing, and radar. He has published extensively. He is the author of Distributed Detection and Data Fusion, published by Springer-Verlag in 1997.
While at the University of Illinois, Dr. Varshney was a James Scholar, a Bronze Tablet Senior, and a Fellow. He is a member of Tau Beta Pi and is the recipient of the 1981 ASEE Dow Outstanding Young Faculty Award. He was elected to the grade of Fellow of the IEEE in 1997 for his contributions in the area of distributed detection and data fusion. In 2000, he received the Third Millennium Medal from the IEEE and Chancellor’s Citation for exceptional academic achievement at Syracuse University. He is the recipient of the IEEE 2012 Judith A. Resnik Award. He received an honorary Doctor of Engineering degree from Drexel University in 2014, ECE Distinguished Alumni Award from UIUC in 2015, and the Yaakov Bar-Shalom Award for Lifetime Excellence in Information Fusion, ISIF in 2018. He was the President of International Society of Information Fusion during 2001.
Peiying Zhu (IEEE Fellow, Nortel/Huawei Fellow)
Senior Director, Huawei, Canada
Plenary Forum Title: AI/ML - A New Paradigm for Communication Networks
Time: 10:00-12:00, Wednesday, February 20, 2019
Dr. Peiying Zhu is an IEEE Fellow and Huawei Fellow. She is currently leading 5G wireless system research in Huawei. The focus of her research is advanced wireless access technologies with more than 200 granted patents. She has been regularly giving talks and panel discussions on 5G vision and enabling technologies. She served as the guest editor for IEEE Signal processing magazine special issue on the 5G revolution and IEEE JSAC on Deployment Issues and Performance Challenges for 5G. She co-chaired various 5G workshops in IEEE GLOBECOM. She is actively involved in 3GPP and IEEE 802 standards development. She is currently a WiFi Alliance Board member.
Prior to joining Huawei in 2009, Peiying was a Nortel Fellow and Director of Advanced Wireless Access Technology in the Nortel Wireless Technology Lab. She led the team and pioneered research and prototyping on MIMO-OFDM and Multi-hop relay. Many of these technologies developed by the team have been adopted into LTE standards and 4G products.
Peiying Zhu received the Master of Science degree and Doctor Degree from Southeast University and Concordia University in 1985 and 1993 respectively.