Kang G. Shin
University of Michigan
As we are entering the era of autonomous driving, vehicular communication remains to be one of the most important development focuses for both governments and industries because it is the backbone for establishing future Intelligent Transportation Systems (ITSs). This talk consists of two main parts. In the first part, I will present the widely-perceived application scenarios of vehicular communications, including vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-everything (V2X), and how they can enhance the safety, mobility and eco-friendliness of future transportation systems. I will also discuss the security threats in vehicular communications and the potential solutions to those threats. In the second part, I will address the acute problem of data verification in vehicular communications. Because using incorrect/compromised information in ITSs can degrade traffic throughput or potentially cause fatal accidents, it is crucial that the receiving end of vehicular communications, especially the infrastructure, must ensure the correctness of the received information. I will discuss the important properties and functions that should be considered in designing a system for data verification. Next, I will introduce DeBi, a novel way of verifying the integrity of vehicular data. Specifically, DeBi determines if the vehicle state data (e.g., vehicle location, speed, acceleration, etc.) carried in the V2I/V2X messages is trustworthy for use in ITSs. It is designed to meet the various requirements of different ITSs and facilitate their future deployment while reflecting app constraints/regulations and adapting to changing environments. It features Adaptive Detection Scope, Adaptive Run-time Operation, Adaptive Deployment, and Individual Data Verification & Reconstruction. Finally, I will conclude this talk by discussing the unresolved issues and potential research directions in vehicular communications.
This is joint with with one of my graduate students, Chun-Yu (Daniel) Chen
Kang Geun Shin (신강근) is the Kevin and Nancy O'Connor Professor of Computer Science and Founding Director of the Real-Time Computing Laboratory in the Department of Electrical Engineering and Computer Science, The University of Michigan, Ann Arbor, Michigan.
At Michigan, he has supervised the completion of 87 PhDs and also chaired the Computer Science and Engineering Division at Michigan for three years starting 1991. From 1978 to 1982 he was on the faculty of Rensselaer Polytechnic Institute, Troy, New York.
He received the B.S. degree in Electronics Engineering from Seoul National University, Seoul, Korea in 1970, and both the M.S. and Ph.D. degrees in Electrical Engineering from Cornell University, Ithaca, New York in 1976 and 1978, respectively.
His current research focuses on QoS-sensitive computing and networks as well as on embedded real-time and cyber-physical systems. He has authored/coauthored more than 980 technical articles and about 60 patents or invention disclosures. He has co-authored (with C. M. Krishna) a textbook ``Real-Time Systems,'' McGraw Hill, 1997. He has received numerous awards, including 2019 Caspar Bowden Award for Outstanding Research in Privacy Enhancing Technologies, and Best Paper Awards from the 2011 ACM International Conference on Mobile Computing and Networking (MobiCom’2011), the 2011 IEEE International Conference on Autonomic Computing, the 2010 & 2000 USENIX Annual Technical Conference, the 2003 IEEE IWQoS, and the 1996 IEEE Real-Time Technology and Application Symposium. He also won the 2003 IEEE Communications Society William R. Bennett Prize Paper Award and the 1987 Outstanding IEEE Transactions on Automatic Control Paper Award. He has also received several institutional awards, including the Research Excellence Award in 1989, Outstanding Achievement Award in 1999, Service Excellence Award in 2000, Distinguished Faculty Achievement Award in 2001, and Stephen Attwood Award in 2004 from The University of Michigan (the highest honor bestowed to Michigan Engineering faculty); a Distinguished Alumni Award of the College of Engineering, Seoul National University in 2002; 2003 IEEE RTC Technical Achievement Award; and 2006 Ho-Am Prize in Engineering.
He has held visiting positions at the U.S. Airforce Flight Dynamics Laboratory, AT&T Bell Laboratories, Computer Science Division within the Department of Electrical Engineering and Computer Science at UC Berkeley, and International Computer Science Institute, Berkeley, CA, IBM T. J. Watson Research Center, Carnegie Mellon University, HP Research Laboratories, Hong Kong University of Science and Technology, Ewha Womans University in Korea, and Ecole Polytechnique Federale de Lausanne (EPFL) in Switzerland.
He is Fellow of IEEE and ACM, and overseas member of the Korean Academy of Engineering, served as the General Co-Chair for 2009 ACM Annual International Conference on Mobile Computing and Networking (MobiCom'09), was the General Chair for 2008 IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON'08), the 3rd ACM/USENIX International Conference on Mobile Systems, Applications, and Services (MobiSys'05) and 2000 IEEE Real-Time Technology and Applications Symposium (RTAS'00), the Program Chair of the 1986 IEEE Real-Time Systems Symposium (RTSS), the General Chair of the 1987 RTSS, a Program Co-Chair for the 1992 International Conference on Parallel Processing, and served numerous technical program committees. He also chaired the IEEE Technical Committee on Real-Time Systems during 1991-93, an Editor of IEEE Trans. on Parallel and Distributed Computing, and an Area Editor of International Journal of Time-Critical Computing Systems, Computer Networks, and ACM Transactions on Embedded Systems. He has also served or is serving on numerous government committees, such as the US NSF Cyber-Physical Systems Executive Committee and the Korean Government R&D Strategy Advisory Committee. He was a co-founder of two startups and is serving as an Executive Advisor for Samsung Research.
High rates have been equated to low delay. We argue that this is too facile an equivalence, and also that it leads to replacing good engineering with more frequency. We provide two examples of such engineering choices and show that the new choices are both far simpler and far more effective than the traditional ones. The first is on error correcting decoding. Generally systems use codes that are much lower rate and far longer length than needed, even when adopting codes that are nominally capacity achieving. We show that even the simplest of codes, already in use for error detection, match or outperform current state of the art code constructions when decoded with a universal noise-centric decoding algorithm, GRAND (Guessing Random Additive Noise Decoding), co-developed with Ken Duffy. The second example shows that replacing hybrid ARQ and ARQ with RLNC (random linear network coding) can provide considerable delay gains. Such gains are even more marked when we use heterogeneous access technologies, such as WiFi with cellular systems.
Muriel Médard is the Cecil H. and Ida Green Professor in the Electrical Engineering and Computer Science (EECS) Department at MIT, where she leads the Network Coding and Reliable Communications Group in the Research Laboratory for Electronics at MIT. She obtained three Bachelors degrees (EECS 1989, Mathematics 1989 and Humanities 1991), as well as her M.S. (1991) and Sc.D (1995), all from MIT. She is a Member of the US National Academy of Engineering (elected 2020), a Fellow of the US National Academy of Inventors (elected 2018), American Academy of Arts and Sciences (elected 2021), and a Fellow of the Institute of Electrical and Electronics Engineers (elected 2008). Muriel was elected president of the IEEE Information Theory Society in 2012, and served on its board of governors for eleven years. She holds an Honorary Doctorate from the Technical University of Munich (2020).
She was co-winner of the MIT 2004 Harold E. Egerton Faculty Achievement Award and was named a Gilbreth Lecturer by the US National Academy of Engineering in 2007. She received the 2017 IEEE Communications Society Edwin Howard Armstrong Achievement Award and the 2016 IEEE Vehicular Technology James Evans Avant Garde Award. She received the 2019 Best Paper award for IEEE Transactions on Network Science and Engineering, the 2018 ACM SIGCOMM Test of Time Paper Award, the 2009 IEEE Communication Society and Information Theory Society Joint Paper Award, the 2009 William R. Bennett Prize in the Field of Communications Networking, the 2002 IEEE Leon K. Kirchmayer Prize Paper Award, as well as eight conference paper awards. Most of her prize papers are co-authored with students from her group.
She has served as technical program committee co-chair of ISIT (twice), CoNext, WiOpt, WCNC and of many workshops. She has chaired the IEEE Medals committee, and served as member and chair of many committees, including as inaugural chair of the Millie Dresselhaus Medal. She was Editor in Chief of the IEEE Journal on Selected Areas in Communications and has served as editor or guest editor of many IEEE publications, including the IEEE Transactions on Information Theory, the IEEE Journal of Lightwave Technology, and the IEEE Transactions on Information Forensics and Security. She was a member of the inaugural steering committees for the IEEE Transactions on Network Science and for the IEEE Journal on Selected Areas in Information Theory. She will serve as the Editor in Chief of the IEEE Transactions in Information Theory starting July 2021.
Muriel received the inaugural 2013 MIT EECS Graduate Student Association Mentor Award, voted by the students. She set up the Women in the Information Theory Society (WithITS) and Information Theory Society Mentoring Program, for which she was recognized with the 2017 Aaron Wyner Distinguished Service Award. She served as undergraduate Faculty in Residence for seven years in two MIT dormitories (2002-2007). She was elected by the faculty and served as member and later chair of the MIT Faculty Committee on Student Life and as inaugural chair of the MIT Faculty Committee on Campus Planning. She was chair of the Institute Committee on Student Life. She was recognized as a Siemens Outstanding Mentor (2004) for her work with High School students. She serves on the Board of Trustees since 2015 of the International School of Boston, for which she is treasurer.
She has over fifty US and international patents awarded, the vast majority of which have been licensed or acquired. For technology transfer, she has co-founded two companies, CodeOn, for which she consults, and Steinwurf, for which she is Chief Scientist.
Muriel has supervised over 40 master students, over 20 doctoral students and over 25 postdoctoral fellows.
We consider the problem of operating wireless networks when packets have deadlines. We present a characterization of capacity as well as optimal scheduling policies for the cases of an access point as well as a network of point-to-point links operating over unreliable channels.
[Joint work with I-Hong Hou, Rahul Singh, and Vivek Borkar]
P. R. Kumar obtained his B.Tech. degree in Electrical Engineering (Electronics) from I.I.T. Madras in 1973, and the M.S. and D.Sc. degrees in Systems Science and Mathematics from Washington University, St. Louis in 1975 and 1977, respectively. From 1977-84, he was a faculty member in the Department of Mathematics at the University of Maryland Baltimore County. From 1985-2011, he was a faculty member in the Department of Electrical and Computer Engineering and the Coordinated Science Laboratory at the University of Illinois. Currently he is at Texas A&M University where he is a University Distinguished Professor, Regents Professor, and O’Donnell Foundation Chair I.
Kumar has worked on problems in game theory, adaptive control, simulated annealing, machine learning, queueing networks, manufacturing systems, scheduling wafer fabrication plants, wireless networks and network information theory. His current research focus includes renewable energy, power systems, security, privacy, automated transportation, unmanned aerial vehicle traffic management, millimeter wave 5G, and cyber-physical systems.
Kumar is a member of the National Academy of Engineering of the USA, the World Academy of Sciences, and the Indian National Academy of Engineering. He was awarded an honorary doctorate by the Swiss Federal Institute of Technology (Eidgenossische Technische Hochschule) in Zurich. He received the IEEE Field Award for Control Systems, the Donald Eckman Award of American Automatic Control Council, the Fred Ellersick Prize of IEEE Communications Society, the Outstanding Contribution Award of ACM SIGMOBILE, the Infocom Achievement Award, and a SIGMOBILE Test-of-Time Paper Award. He is an ACM Fellow and a Fellow of IEEE. He is an Honorary Professor at IIT Hyderabad, and Franklin Woeltge Professor Emeritus at the University of Illinois, Urbana-Champaign. He was awarded the Distinguished Alumnus Award from IIT Madras, the Alumni Achievement Award from Washington University in St. Louis, and the Daniel C. Drucker Eminent Faculty Award from the College of Engineering at the University of Illinois.
The upheaval of AI applications has spawned a great many innovations across the whole stack, including the design of neural network (NN) accelerators, runtime frameworks, novel NN algorithms, and diverse scenario-specific solutions. The active use and interaction of these innovations on edge computing, which has strict requirements on latency and energy consumption, have raised unique challenges and opportunities compared to the cloud-side counterpart.
This talk will center on these challenges and opportunities, and introduce the works of our team towards empowering intelligent edge computing. The talk will cover our projects on the edge NN-runtime implementation which fully consider the features of heterogeneous edge hardware to gain substantial speedup, the hardware-aware NN algorithm design to achieve real efficiency on diverse edge NN deployment platforms, and the end-to-end solutions for specific edge AI applications.
Ting Cao is a Senior Researcher in Microsoft Research Asia (MSRA). Her research interests include deep learning system and algorithm design, hardware/software co-design, high-level language implementation, management of heterogeneous hardware, and big data systems. She received her PhD from Research School of Computer Science, the Australian National University. Before joining MSRA, she had been working in the State Key Lab of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, and then the Compiler and Computing Language Lab in 2012 Labs, Huawei Technologies, where she was engaged in the OpenArk (Fangzhou) compiler project. She had publications in a series of top venues, including ISCA, ASPLOS, PLDI, MobiCom, MobiSys, etc. She has also served in the program committee in several top conferences, such as PLDI, OOPSLA, and ISMM.
Lead researcher in Alibaba XG Lab
The COVID19 pandemic has profoundly changed the landscape of videos and redefined how we watch, what we watch and why we watch. For example, hundreds of millions of users watch videos on Taobao to decide what to buy, collaborate with colleagues remotely via video conferencing on Dingtalk, enjoy precious leisure minute with Youku, and receive education in streaming classroom powered by AliCloud. These revolutions put new pressures and requirements on the network in terms of bandwidth, latency, scalability, and reliability. However, there is a big gap. The truth is, today's network is far from being the "ideal pipe", and we suffer, more or less, from the agony of unstable, slow, and failed network connectivity. In this talk, I will discuss the lessons we learned from Alibaba video services over the past two years and the insights we gained to close this gap. Finally, I will discuss WAVE, a wireless and video entanglement framework, how WAVE addresses these challenges, and the opportunities in the future path.
Yunfei is a lead researcher at XG Lab in the Alibaba Damo Academy and he is now responsible for building the next generation mobile transport protocols and algorithms. At Alibaba, he and his colleagues developed XLINK, the first QoE-driven multi-path QUIC transport protocol, and NFC+, the world’s longest NFC communication system. Before joining Alibaba, he was a postdoctoral researcher at MIT Media Lab. He received Ph.D. in Electrical and Computer Engineering from Cornell University and B.S. from USTC. He has published more than 10 papers on top conferences including SIGCOMM, MOBICOM and NSDI and he holds more than 15 granted US patents. His research has been covered by media outlets including BBC, The Verge, MIT Technology Review, the CBS Morning and IEEE Spectrum. His work NFC+ has been named the top ten RFID breakthroughs in the year of 2020.
The digital world offers infinite possibilities, which forces us to make choices all the time: we click on a search result in a list, choose a song from iTunes, or pick a restaurant on Yelp. Because of the central role of choosing, there is an abundance of data capturing comparisons and rankings. This has led to a resurgence of interest in discrete-choice models in the machine learning community. In this talk, we discuss several recent results in this context.
We discuss large-scale inference of the Plackett-Luce (PL) model, a widely used probabilistic choice model, via an iterative spectral algorithm. We also consider the PL model in an active learning setting, where we can ask an oracle comparison questions and receive noisy answers. Our goal is to recover the underlying ranking accurately by asking as few questions as possible. When we constrain choices to a network setting, capturing the process of navigating on a graph using local information, we obtain a new node metric termed ChoiceRank that estimates link traffic (or strength) more faithfully than other alternatives, such as PageRank.
We then explore several variations of choice models embedded in concrete applications. In peer production systems, such as a large collaborative software project or a parliament writing laws, such models combine predictive performance with interpretable parameters. In interactive search, we are able to find a target in a large database without formulating a query.
Matthias Grossglauser is a Professor of Computer and Communication Sciences at EPFL in Lausanne, Switzerland, where he co-directs the Information and Network Dynamics lab. His current research interests center on machine learning and data analytics, and on applications of these technologies in network science, computational social sciences, and recommender systems.
He is currently a commissioner of the Swiss Federal Communications Commission ComCom. He was the director of EPFL's Doctoral School in Computer and Communication Sciences (2016-2019). From 2007-2010, he was with the Nokia Research Center (NRC) in Helsinki, Finland, leading the Internet Laboratory, a research organization comprising seven teams in security, networking, social media, and user experience. He was also in charge of a tech-transfer program focused on applied data mining and machine learning, and served on Nokia's CEO Technology Council, a team of technology experts advising the Nokia CEO. Prior to this, he was Assistant Professor at EPFL, and Principal Research Scientist in the Networking and Distributed Systems Laboratory at AT&T Research (Shannon Labs) in New Jersey, USA.
He is a Fellow of the IEEE, and the recipient of the 1998 Cor Baayen Award from the European Research Consortium for Informatics and Mathematics (ERCIM) and of the 2006 CoNEXT/SIGCOMM Rising Star Award. He was associate editor of the IEEE/ACM Transactions on Networking (2001-2003).