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rings [2023/07/20 15:58]
lingjialiu
rings [2023/07/21 14:51] (current)
lingjialiu
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 ===== Personnel ===== ===== Personnel =====
  
-  * PI in the Leading Institution: ​[[https://​computing.ece.vt.edu/​~lingjialiu/​doku.php|Lingjia Liu]] (ECE at VT+  * [[https://​computing.ece.vt.edu/​~lingjialiu/​doku.php|Lingjia Liu]]ECE at VTPI in the Leading ​Institution 
-  * PI in the Collaborative ​Institution[[https://​ece.duke.edu/​faculty/​robert-calderbank|Robert Calderbank]] ​(ECE/CS at Duke+  * [[https://​ece.duke.edu/​faculty/​robert-calderbank|Robert Calderbank]]ECE/CS at DukePI in the Collaborative Institution 
-  * PI in the Collaborative Institution[[https://​users.ece.cmu.edu/​~yuejiec/​|Yuejie Chi]] (ECE at CMU)+  * [[https://​users.ece.cmu.edu/​~yuejiec/​|Yuejie Chi]]ECE at CMU: PI in the Collaborative Institution 
 +  * Shadab Mahboob, ECE at VT: PhD student 
 +  * Beyza Dabak, ECE at Duke: PhD student 
 +  * Shicong Cen, ECE at CMU: PhD Student
  
 +===== Industry Collaborators =====
  
 +  * Santhosh Kumar Vanaparthy
 +  * Jan Schreck
 +  * Bin Li
 +  * Zhu Zhou
 +  * Jerry Syder
  
 ===== Synopsis ===== ===== Synopsis =====
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 Designing waveforms in a different domain (e.g., the delay-Doppler domain) creates a channel that only changes at the speed of the local propagation environment,​ enabling detection, estimation and learning at the speed of NextG, and migration of MIMO scheduling to the cloud. Reservoir computing-based real-time machine learning tools will be coupled with new waveform design to enable receive processing at the speed of NextG. Novel multi-agent reinforcement learning-based centralized scheduling strategies will be introduced to enable low-complexity and resilient resource allocation in the delay-Doppler domain. A new suite of distributed and resilient machine learning algorithms that are communication-efficient and heterogeneity-aware will be tailored to information processing in GAINs. Designing waveforms in a different domain (e.g., the delay-Doppler domain) creates a channel that only changes at the speed of the local propagation environment,​ enabling detection, estimation and learning at the speed of NextG, and migration of MIMO scheduling to the cloud. Reservoir computing-based real-time machine learning tools will be coupled with new waveform design to enable receive processing at the speed of NextG. Novel multi-agent reinforcement learning-based centralized scheduling strategies will be introduced to enable low-complexity and resilient resource allocation in the delay-Doppler domain. A new suite of distributed and resilient machine learning algorithms that are communication-efficient and heterogeneity-aware will be tailored to information processing in GAINs.
 ### ###
 +
 +===== Education and Outreach Activities =====
 +
 +  * PI Liu, WiOPT Workshop on the Machine Learning in Wireless Communication Networks (WMLC), Real-Time Machine Learning for MIMO-OFDM: Symbol Detection Using Reservoir Computing, September 2022
 +  * PI Liu, 4th Buffalo Day for 5G and Wireless Internet of Things, Real-Time Machine Learning for MIMO-OFDM: Symbol Detection Using Reservoir Computing, November 2022
 +  * PI Calderbank, NextG Alliance, Online Seminar: Learning in the Delay Doppler Domain, September 2022
 +  * PI Calderbank, USC Viterbi School of Engineering,​ The Viterbi Lecture: Learning to Communicate,​ March 2023
 +  * PI Calderbank, University of Arizona: Learning to Communicate,​ March 2023
 +  * PI Calderbank, Apple Online Seminar: Learning to Communicate,​ April 2023
 +  * PI Calderbank, White House 6G Wireless Forum at the National Science Foundation: Panelist, Federal Funding Panel (moderated by Margaret Martonosi) with Peter Vetter (President, Bell Labs Core Research)
 +  * PI Calderbank, UCLA: Learning to Communicate,​ May 2023
 +  * PI Calderbank, Nokia Bell Labs, Online Seminar: Learning to Communicate,​ May 2023
 +  * PI Calderbank, Middle East Technical University (METU) Online Seminar: Learning to Communicate,​ May 2023
 +  * PI Chi, CVPR Workshop on Federated Learning for Computer Vision (FedVision):​ Coping with Heterogeneity and Privacy in Communication-Efficient Federated Optimization,​ June 2022
 +  * PI Chi, Lehigh University: Coping with Heterogeneity and Privacy in Communication-Efficient Federated Optimization,​ October 2022
 +  * PI Chi, Cornell University: Multi-agent Reinforcement Learning: Statistical and Optimization Perspectives,​ November 2022
 +  * PI Chi, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Tutorial: Advances in Federated Optimization:​ Efficiency, Resiliency, and Privacy, June 2023
  
 ===== Publication ===== ===== Publication =====
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   - Ao, R. and Cen, S. and Chi, Y. "​Asynchronous Gradient Play in Zero-Sum Multi-agent Games" International Conference on Learning Representations (ICLR) , 2023   - Ao, R. and Cen, S. and Chi, Y. "​Asynchronous Gradient Play in Zero-Sum Multi-agent Games" International Conference on Learning Representations (ICLR) , 2023
   - Cen, S. and Chi, Y. and Du, S. and Xiao, L. "​Faster Last-iterate Convergence of Policy Optimization in Zero-Sum Markov Games" International Conference on Learning Representations (ICLR) , 2023   - Cen, S. and Chi, Y. and Du, S. and Xiao, L. "​Faster Last-iterate Convergence of Policy Optimization in Zero-Sum Markov Games" International Conference on Learning Representations (ICLR) , 2023
 +
 +===== Code Repository =====
 +
rings.1689883094.txt.gz · Last modified: 2023/07/20 15:58 by lingjialiu