Academic Profile : Faculty

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Assoc Prof Tay Wee Peng
Associate Chair (Academic), School of Electrical & Electronic Engineering
Associate Professor, School of Electrical & Electronic Engineering
 
 
Dr Tay Wee Peng received the B.S. degree in Electrical Engineering and Mathematics, and the M.S. degree in Electrical Engineering from Stanford University, Stanford, CA, USA, in 2002. He received the Ph.D. degree in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology, Cambridge, MA, USA, in 2008. He is currently an Associate Professor in the School of Electrical and Electronic Engineering at Nanyang Technological University, Singapore. His research interests include information and signal processing over networks, distributed inference and estimation, information privacy, machine learning, information theory, and applied probability. Dr. Tay received the Tan Chin Tuan Exchange Fellowship in 2015. He is a coauthor of the best student paper award at the Asilomar conference on Signals, Systems, and Computers in 2012, and the IEEE Signal Processing Society Young Author Best Paper Award in 2016. He was an Associate Editor for the IEEE Transactions on Signal Processing (2015 -- 2019), and is currently an Associate Editor for the IEEE Transactions on Signal and Information Processing over Networks, an Editor for the IEEE Transactions on Wireless Communications and IEEE Open Journal of Vehicular Technology.
- Signal and Information Processing over Networks
- Statistical Privacy for Networks
- Inference and Learning
- Detection, Estimation and Optimization
 
  • 5G-V2X Communication Trial and Use Cases Development
  • Design and Reinforcement Security on Smart Grids Against Cyber-Physical Attack
  • E-Commerce Knowledge Graph
  • Feasibility Study Of Multi-Function Millimeter-Wave RaCoPo System
  • Generalized Message Passing for Federated Learning in Multi-Access Edge Computing
  • Geography of Fake News: Misinformation Spreading and Implication for Capital Markets
  • Graph Signal Processing for High Dimensional Structures and Spaces
  • Next-Generation V2X Network Architecture and Ecosystem for Smart Mobility
  • Project LOCUS
  • Trust, failure and trust recovery in financial exchanges: towards a culturally-intelligent, dynamic and sensing AI
US 2020/0146103 A1: Wireless Sensor Network And Parameter Optimization Method Thereof (2020)
Abstract: A wireless sensor network includes an aggregator, a control device, a bridge device, and a mesh module. The control device is connected with the aggregator and the bridge device. The mesh module is wirelessly connected with the bridge device and the control device. A mesh network is built by the connections of the mesh module, the bridge device, and the control device. A duty cycle of the mesh module is less than or substantially equal to 10 percent. A command sent by the aggregator is converted into a wireless message by the control device, the wireless message is transmitted by the control device and retransmitted through a first amount of radios and repeated for a second amount of times by the bridge device, so that the wireless message is successfully received by the mesh module. Therefore, a mesh network with high efficiency and low cost is achieved.