Wei Shi

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Associate Professor

Carleton University

Canada

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About

Wei Shi is an Associate Professor at Carleton University in Canada. His research interests encompass federated learning, blockchain technology, communication networks, and cyber security. He has contributed to various recent publications, including works on combinatorial contextual neural bandits, writing style change detection, and quantum-inspired edge intelligence. Dr. Shi is actively exploring innovative solutions for future internet services and enhancing network security.

Recent Grants

Grant: Close

Improving Big Data Infrastructure to Effectively Utilize Resources for Data Processing and Services

Open Date: 2015-05-01

Close Date: 2020-05-01

Grant: Close

Improving Big Data Infrastructure to Effectively Utilize Resources for Data Processing and Services

Open Date: 2015-05-01

Close Date: 2020-05-01

Articles (17)

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2025

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2022

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2022

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2022

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2022

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2022

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2022

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2022

Collaborators (2)

Anastasios Papazafeiropoulos

University of Hertfordshire

UNITED KINGDOM

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Pandelis Kourtessis

University of Hertfordshire

UNITED KINGDOM

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Wei Shi

Research Interests

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About

Recent Grants

Articles (17)

An Enhanced Combinatorial Contextual Neural Bandit Approach for Client Selection in Federated Learning

A Survey on Writing Style Change Detection: Current Literature and Future Directions

An Attack Exploiting Cyber-Arm Industry

The Permissioned Blockchain-Based Quantum-Inspired Edge Intelligence Approach for the Services of Future Internet of Vehicles

Balancing Power Grids and Maximizing Revenue: A Novel Approach to Rebate Auctions for Cloud Workload Migrations

Blockchain-Based Intelligence Networking for Cooperative Positioning Towards Future Internet of Vehicles

Dynamic Virtual Network Embedding Leveraging Neighborhood and Preceding Mappings Information

Do Not Trust the Clouds Easily: The Insecurity of Content Security Policy Based on Object Storage

Near-Field Terahertz Communications: Model-Based and Model-Free Channel Estimation

Terahertz-Band Channel and Beam Split Estimation via Array Perturbation Model

An Anonymity Vulnerability in Tor

Multiaccess Edge Integrated Networking for Internet of Vehicles: A Blockchain-Based Deep Compressed Cooperative Learning Approach

A comparative analysis of CGAN‐based oversampling for anomaly detection

Network intrusion detection using machine learning approaches: Addressing data imbalance

Auction-Based Scheduling of Excess Energy Consumption to Enhance Grid Upward Flexibility

Deep Reinforcement Learning for Stability Enhancement of a Variable Wind Speed DFIG System †

Differential Privacy via a Truncated and Normalized Laplace Mechanism

Collaborators (2)

Anastasios Papazafeiropoulos

Pandelis Kourtessis

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