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CESG Fishbowl Seminar: Coded Caching and Ruzsa-Szemerédi Graphs

CESG Fishbowl Seminar: Coded Caching and Ruzsa-Szemerédi Graphs

June 1 @ 11:30 am - 12:30 pm

Presenter: Karthikeyan Shanmugam of IBM Research AI in New York Title: Coded Caching and Ruzsa-Szemerédi Graphs Abstract: Coded caching is a problem where encoded broadcasts are used to satisfy users requesting popular files and having caching capabilities. Recent work by Maddah-Ali and Niesen showed that it is possible to satisfy a scaling number of users with only a constant number of broadcast transmissions by exploiting coding and caching. One of the outstanding issues is that the schemes known for this problem required the splitting of files into an exponential number of packets before the significant coding gains of caching appeared. The question of what can be achieved with polynomial subpacketization (in the number of users) has been a central open problem in this area. We resolve this problem and present the first coded caching scheme with polynomial (in fact, linear) subpacketization. We obtain a number of transmissions that is not constant, but can be any polynomial in the number of users with an exponent arbitrarily close to zero. Our central technical tool is a novel connection between Ruzsa-Szem\’eredi graphs and coded caching. However, this scheme requires the number of users to be very large. In recent years, search for schemes that are efficient in terms of file size has gathered considerable interest. We show that many existing schemes that optimize the number of file sub-packets can also be cast in the framework of Ruzsa-Szemeredi graphs. We also discuss remaining open problems in tackling this important practical issue. Biography: Karthikeyan Shanmugam is currently a Research Staff Member at IBM Research NY in the AI Science group. Previously, he was a Herman Goldstine Postdoctoral Fellow in the Math Sciences Division at IBM Research, NY. He obtained his Ph.D. in Electrical and Computer Engineering from UT Austin in 2016 under the supervision of Dr. Alex Dimakis. Prior to this, he obtained his MS degree from USC, B.Tech and M.Tech degrees from IIT Madras. His research interests broadly lie in Statistical Machine learning, Graph Algorithms, Coding Theory and Information Theory. In machine learning, his current research focus is on Causal inference, Online Learning and Interpretability in ML. In Information theory, he focuses on problems related to caching in wireless networks.  


CESG Fishbowl Seminar: “Systems of Unmanned Vehicles for Persistent Service: Concepts, Task Allocation, Design & Prototype Components”

CESG Fishbowl Seminar: “Systems of Unmanned Vehicles for Persistent Service: Concepts, Task Allocation, Design & Prototype Components”

June 18 @ 10:00 am - 11:00 am

Dr. James R. Morrison Associate Professor in the Department of Industrial and Systems Engineering (ISysE) at KAIST, South Korea   Key points to include: – Task allocation in multi-robot systems with logistics constraints for persistent operations – Extended VRP models with various solution methodologies ranging from formal branch & price to fast accurate heuristics – Stochastic control methods including dynamic programming, heuristics and learning to plan – System design approaches in both the deterministic and stochastic contexts – Implementation efforts including prototypes of system modules   Abstract: The capabilities of modern affordable unmanned aerial vehicles (UAVs) include vision based navigation, GPS tracking and cargo delivery among a host of other functions. Yet, despite these capabilities, their promise is limited by a finite energy source and restricted payload. To overcome the limitations of a single vehicle, a system consisting of a fleet of vehicles and replenishment service stations can be used to accomplish larger scale mission objectives. Persistent systems of unmanned vehicles can provide services including security escort, search and rescue and border patrol. An essential function required for the operation of a system of unmanned vehicles is task allocation. If system resources are allocated wisely, more can be accomplished. For the task allocation problem, we will discuss both deterministic and stochastic centralized optimization approaches. In the deterministic context, we discuss column generation, branch and bound, receding horizon task allocation (RHTA) and custom heuristic approaches. In the stochastic context, we discuss dynamic programming, reinforcement learning, heuristic and learning algorithms. Another key consideration is system design: how many resources are required and where should they be deployed? We discuss how task allocation methods can be extended to include design. A more computationally tractable approach considers system design at a higher level of abstraction. We provide an overview of work to address the design problem for multiple service stations and numerous customer service sites. Heuristic solution methods based on the classic savings algorithm combined with a Voronoi decomposition are compared with complete enumeration. Finally, we discuss our efforts to develop a system of UAVs and service stations to serve as an automated security escort system at KAIST. Such a system consists of many components including UAVs, service stations, a customer service request app, GPS tracking software, vision tracking software, a simple AI for each UAV and a central task allocation system. The progress on this system, its components and related issues will be reviewed.   Biography: Dr. James R. Morrison (james.morrison@kaist.edu, http://xS3D.kaist.edu) received his Ph.D. in Electrical and Computer Engineering, from the University of Illinois at Urbana-Champaign, USA. He is currently an Associate Professor in the Department of Industrial and Systems Engineering (ISysE) at KAIST, South Korea. Since 2016, he has served as the Director of KICEP at KAIST. His research interests include persistent UAV service, Industry 4.0 and education. He has published over 90 peer reviewed journal and conference papers in these areas. He has received teaching awards including the KAIST Creative Teaching (Grand Prize) Award in 2012 and the KAIST ISysE […]

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