Wee Peng Tay

Assoc. Professor

Nanyang Technological University (NTU)

Singapore

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About

Wee Peng Tay is an Associate Professor at Nanyang Technological University in Singapore. His research areas encompass neuromorphic sensing, power systems optimization, trajectory prediction, and advanced graph neural networks. His recent work includes contributions to wireless implantable brain-machine interfaces, risk dispatch in power systems, and multi-modal place recognition.

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2025

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2022

Collaborators (3)

Yanan Zhao

Nanyang Technological University

SINGAPORE

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Antonio Ortega

University of Southern California

UNITED STATES

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Joey Tianyi Zhou

Adj Associate Professor

National University of Singapore (NUS)

SINGAPORE

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Wee Peng Tay

Research Interests

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About

Articles (20)

Towards neuromorphic compression based neural sensing for next-generation wireless implantable brain machine interface

Uncertain Interval-Based Risk Dispatch Approach of Power Systems Under an Unified Framework of Multiple Uncertainties

Lane-Aware Dynamic Spatio-Temporal Transformer for Unimodal Trajectory Prediction

Customized OTFS Pulse Compression and Waveform Optimization for Enhancing Target Sensing in ISAC Systems

Graph Distributional Signals for Regularization in Graph Neural Networks

High-Performance Optimization Model Based on Novel Conditional Value At Risk Metric for Power Grids With High Wind Power Penetration

PRFusion: Toward Effective and Robust Multi-Modal Place Recognition With Image and Point Cloud Fusion

Multi-armed linear bandits with latent biases

Integrated Transmit Waveform and RIS Phase Shift Design for LPI Detection and Communication

Kernel Based Reconstruction for Generalized Graph Signal Processing

Graph Neural Networks With Triple Attention for Few-Shot Learning

Resilience-Oriented Control for Cyber-Physical Hybrid Energy Storage Systems Using a Semiconsensus Scheme: Design and Practice

Approximate Maximum-Likelihood RIS-Aided Positioning

Graph Signal Processing Over a Probability Space of Shift Operators

Image Patch-Matching With Graph-Based Learning in Street Scenes

LPI Radar Signal Design Resistant to Identification by ESM Systems

A Tightly Coupled Integration Approach for Cooperative Positioning Enhancement in DSRC Vehicular Networks

An Unsupervised Bayesian Neural Network for Truth Discovery in Social Networks

Unlimited dynamic range signal recovery for folded graph signals

Unlimited dynamic range signal recovery for folded graph signals

Collaborators (3)

Yanan Zhao

Antonio Ortega

Joey Tianyi Zhou

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