RESEARCH KEYNOTE SERIES
(Professor, Purdue University)
Bio: Ninghui Li is Samuel D. Conte Professor of Computer Science at Purdue University. He received a Bachelor's degree from the University of Science and Technology of China (USTC)'s Special Class of Gift Young in 1993, and a Ph.D. in Computer Science from New York University in 2000. His research interests are in security and privacy, on which he has published over 180 referred papers. He is currently Editor-in-Chief for ACM Transactions On Privacy and Security, and is on the editorial boards of Journal of Computer Security (JCS) and ACM Transactions on Internet Technology (TOIT). He served as Chair of ACM Special Interest Group on Security, Audit and Control (SIGSAC) from 2017 to 2021, and is an IEEE Fellow.
Title of Talk: Analyzing Proximity-based Contact Tracing Protocols
Abstract: The COVID-19 pandemic is shaping up to be among the biggest disasters for humanity since World War 2. Traditional contact tracing requires extensive manual efforts and scales poorly. As smartphone usage is ubiquitous in modern society, the use of smartphone apps to help with contact tracing is a promising solution. In Proximity-based Contact Tracing (PCT) systems, the goal is to identify events where two persons are physically close to each other, by detecting that their smartphones are physically adjacent for some period of time. The challenge is how to do this while balancing Privacy of individuals, Resiliency to malicious attackers, and Efficiency. In this talk, we present a systematic approach to design and analyze PCT protocols. We identify a list of desirable properties of a contact tracing design from the four aspects of Privacy, Utility, Resiliency, and Efficiency (PURE). We also identify two main design choices for PCT protocols: what information patients report to the server, and which party performs the matching of patient and contacts. These two choices determine most of the PURE properties and enable us to conduct a comprehensive analysis and comparison of the existing protocols. This also led us to discover a new design and offers attractive combination of properties. See https://arxiv.org/abs/2012.09520 for the paper.
(Professor, Massachusetts Institute of Technology)
Bio: 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).
Title of Talk: Universal decoding - towards escaping standards for coding.
Abstract: Much of the recent debate on the adoption of 5G wireless technology has centered on the issue of standards for error-correcting codes. The justification for such standardization is largely based on the historical presumption that error-correcting codes require specialized, complex decoders implemented in efficient, dedicated and customized chips. We shall show in this talk this presumption no longer holds. “Guessing Random Additive Noise Decoding," or GRAND is a new method
developed by Duffy, Médard and their research groups. It is a universal, code-agnostic decoding for the type of low to moderate redundancy settings that are suitable in most commercial communication channels. Recent work with Yazicigil and her group demonstrated that GRAND can be implemented in silicon with extremely low latency and energy. GRAND enables a new exploration of code performance, independently of tailored decoders. Surprisingly, even the simplest of codes, such as random linear codes or codes currently broadly used just for error checking, do as well as state-of-the-art codes.
(Professor, Carnegie Mellon University)
Bio: Mor Harchol-Balter is the Bruce J. Nelson Professor of Computer Science at Carnegie Mellon. She received her Ph.D. from U.C. Berkeley in 1996, joined CMU in 1999, and served as the Head of the Ph.D. program from 2008-2011. Mor is a Fellow of both ACM and IEEE. She is a recipient of the McCandless Junior Chair, the NSF CAREER award, and several teaching awards, including the Herbert A. Simon Award and Spira Teaching Award. She is a recipient of dozens of Industrial Faculty Awards including multiple awards from Google, Microsoft, IBM, EMC, Facebook, Intel, Yahoo!, and Seagate. Mor's work focuses on designing new resource allocation policies, including load balancing policies, power management policies, and scheduling policies, for distributed systems. Mor is heavily involved in the SIGMETRICS / PERFORMANCE research community, where she has received many paper awards (SIGMETRICS 21, SIGMETRICS 19, PERFORMANCE 18, INFORMS APS 18, EUROSYS 16, MASCOTS 16, MICRO 10, SIGMETRICS 03, ITC 03, SIGMETRICS 96). She is also the author of a popular textbook, Performance Analysis and Design of Computer Systems , published by Cambridge University Press, which bridges Operations Research and Computer Science.
Title of Talk: Recent Breakthroughs in Stochastic Scheduling Theory
Abstract: This talk considers stochastic scheduling, where job sizes and arrival times are drawn from a distribution. As empirical job size variability has skyrocketed, stochastic scheduling research has grown increasingly important. What scheduling policies should we use to keep response times low? How should we schedule when job sizes are unknown or only partially known? What scheduling policies should we use in a multi-server (M/G/k) setting, as compared with a single-server (M/G/1) setting? How can we analyze the response times of scheduling policies in single-server and multi-server settings? In this talk, we discuss recent breakthroughs over the last 3 years in the area of stochastic scheduling. These include:
(1) The SOAP scheduling framework, which greatly expands the class of scheduling policies whose response times we can now analyze in the M/G/1 setting.
(2) The first response time analysis of common scheduling policies in the M/G/k.
(3) Asymptotically optimal scheduling in the M/G/k.
(Professor, Stanford University)
Bio: Prof. Krishna Saraswat is the Rickey/Nielsen Chair Professor of Electrical Engineering at Stanford University. He received Ph.D. from Stanford University and B.E. from BITS, Pilani. His research interests are in new and innovative materials, structures, and process technology of semiconductor devices and metal and optical interconnects for nanoelectronics, and high efficiency and low cost solar cells. He has supervised more than 95 doctoral students, 40 post doctoral scholars and has authored or co-authored over 800 technical papers. He is a Life Fellow of the IEEE and has received many awards including The Electrochemical Society Thomas Callinan in 2000, the 2004 IEEE Andrew Grove Award, the Technovisionary Award from the India Semiconductor Association in 2007, SIA Researcher of the Year Award in 2012 and SRC Aristotle Award in 2021. He is listed by ISI as one of the Highly Cited Authors in his field.
Title of Talk: Emerging Interconnect Technologies for Nanoelectronics
Abstract: While dimension scaling, introduction of new materials and novel device structures has enhanced the transistor performance, the opposite is true for the interconnects. Looking into the future the relentless scaling paradigm is threatened by the limits of copper/low-k interconnects. Thus, it is imperative to examine alternate interconnect schemes and explore possible advantages of novel potential candidates. Carbon nanotubes, optical interconnects and three-dimensional (3-D) heterogeneous integration have emerged as potential candidates to augment copper/low-k interconnects and mitigate the interconnect tyranny by providing lower power dissipation, improved communication bandwidth, and signal latency. This talk will focus on the most important devices and technologies for integration of these on the silicon platform.
Tsachy (Itschak) Weissman
(Professor, Stanford University)
Bio:Tsachy has been on the faculty of the Electrical Engineering department at Stanford since 2003, conducting research in and teaching the science of information, with applications spanning genomics, neuroscience, and technology. He has served and still does on editorial boards for scientific journals, technical advisory boards in industry, and as founding director of the Stanford Compression Forum. His recent initiatives at Stanford include the STEM2SHTEM science and humanities high school internship program, and Stagecast, a low-latency video platform allowing actors and singers to perform together in real-time while geographically distributed. IEEE fellow, he has received multiple awards for his research and teaching, including best paper awards from the IEEE Information Theory and the Communications societies, and best student authored paper awards in the top conferences of his areas of scholarship. He has prototyped Guardant Health's first algorithms for early detection of cancer from blood tests, and has more recently co-founded and sold Compressable to Amazon, where he is now an Amazon Scholar. His favorite gig to date was being an advisor to the HBO show “Silicon Valley”.
Title of Talk: Human Inspired Compression of Multimedia Data
Abstract: I'll describe recent and ongoing work in my group and by others, suggesting potential for substantial improvements over current approaches to multimedia data compression and streaming. The idea is to be attentive to and emulate ways in which humans perceive and describe the physical world around them.
(Professor, Harvard University)
Bio: HT Kung is William H. Gates Professor of Computer Science and Electrical Engineering at Harvard. As a volunteer, he also serves as the President of Taiwan AI Academy. Professor Kung has pursued various research interests in his career, including those related to this presentation, such as VLSI design, systolic arrays, parallel computing, computer architecture, embedded deep learning, and distributed computing. Professor Kung’s academic honors include Member of the National Academy of Engineering (US), Member of Academia Sinica (Taiwan), Guggenheim Fellowship, and the ACM SIGOPS 2015 Hall of Fame Award.
Title of Talk: Hardware Algorithms for AI Accelerators
(Professor, ETH Zurich)
Bio: Prof Dr. Carlo Menon has a Laurea degree in Mechanical Engineering and a PhD in Space Sciences and Technologies from the University of Padua in Italy. Carlo was a visiting graduate student at Carnegie Mellon University in the USA and spent a few years as a Research Fellow & Technical Officer in the Advanced Concepts Team at the European Space Agency (ESA) in the Netherlands. From there, he moved to Simon Fraser University in Canada, where he received tenure in 2012, and set up the Menrva Research Group (40+ researchers). He is currently Full Professor and Head of the Biomedical and Mobile Health Technology lab at ETH Zurich. Among others, Carlo has been awarded a Tier I Canada Research Chair and career awards from both the Canadian Institutes of Health Research (CIHR) and the Michael Smith Foundation for Health Research (MSFHR). He has also been the recipient of over 80 competitive grants and published over 400 scientific works. Outside the lab, he likes exploring new corners of scenic Switzerland. Carlo has a strong interest in translating scientific discovery into impactful technologies — he has co-founded two start-up companies.
|Full Paper Submission:||7th November 2021|
|Acceptance Notification:||17th November 2021|
|Final Paper Submission:||24th November 2021|
|Early Bird Registration||21st November 2021|
|Presentation Submission:||23rd November 2021|
|Conference:||1 - 4 December 2021|
• Conference Proceedings will be submitted for publication at IEEE Xplore® digital library.
• Best Paper Award will be given for each track.
• Conference Record No 53757