Abstract: To support faster and more efficient networks, mobile operators and service providers are bringing 5G millimeter wave (mmWave) networks indoors. However, due to their high directionality, mmWave links are extremely vulnerable to blockage by walls and obstacles. Meanwhile, the first low earth orbit satellite networks for internet service have recently been deployed and are growing in size, yet will face deployment challenges in many practical circumstances of interest. To address both such challenges, the Princeton Advanced Wireless Systems lab is exploiting advances in artificially-engineered metamaterials to enable steerable wireless mmWave and Ku band beam reflection and refraction. Our approaches fall under the category of Huygens metamaterials, but our advances enable practical electronic control and simultaneous use in multiple frequency bands in such materials, for the first time. We have specified our designs in RF simulators and prototyped in hardware, and our experimental evaluation demonstrates up to 20 dB SNR gains over environmental paths in an indoor office environment.

BIO: Kyle Jamieson is Professor of Computer Science and Associated Faculty in Electrical and Computer Engineering at Princeton University. His research focuses on mobile and wireless systems for sensing, localization, and communication, and on massively-parallel classical, quantum, and quantum-inspired computational structures for NextG wireless communications systems. He received the B.S. (Mathematics, Computer Science), M.Eng. (Computer Science and Engineering), and Ph.D. (Computer Science, 2008) degrees from the Massachusetts Institute of Technology. He then received a Starting Investigator fellowship from the European Research Council, a Google Faculty Research Award, and the ACM SIGMOBILE Early Career Award. He served as an Associate Editor of IEEE Transactions on Networking from 2018 to 2020. He is a Senior Member of the ACM and the IEEE.

Abstract: In recent years, there is a growing interesting in designing efficient online learning algorithms, either in terms of multi-armed bandits or reinforcement learning approach. In this talk, we will give a brief background about multi-armed bandits (MABs), as well as the intuition on why it works and what core mathematical techniques we use. Then we will discuss the case wherein we have multiple heterogeneous learners who is willing to cooperate to solve the same instance of a K-armed stochastic bandit problem.

BIO: John C.S. Lui is current the Choh-Ming Li Chair Professor of the CSE Department at the Chinese University of Hong Kong. He is a fellow of ACM and IEEE, and a fellow in the Hong Kong Academy of Engineering Sciences. He is a senior research fellow at the Croucher Foundation. His recent research interests include quantum Internet, online learning algorithms and their applications, as well as stochastic performance evaluation theory.

Abstract: TBD

BIO: Dr. Xinbing Wang is a Professor of Shanghai Jiao Tong University. He is the Chairman of the ACM China Council, was the Vice Dean of the School of Electronic Information and Electrical Engineering, as well as the Executive Director of the John Hopcroft Center for Computer Science at Shanghai Jiao Tong University. He is leading the project of ACEMAP (www.acemap.info), an academic knowledge graph system with billion nodes. He has served on the editorial boards of several international journals, including IEEE Transactions on Information Theory, IEEE/ACM Transactions on Networking. He has supervised three Ph.D. students awarded as ACM China Doctoral Dissertation, Outstanding Doctoral Dissertation Award from Chinese Institute of Electronics (CIE) and Outstanding Doctoral Dissertation Award from Chinese Institute of Communications (CIC), and more than 40 graduated students working in various universities in China.