Dr.is a lecturer in the Department of Computer Science at the University of Roehampton in the UK and also serves as the course director for the Website Design and Development major. She has over 8 years of research and teaching experience, with research areas covering edge computing, the Internet of Things, federated learning, digital twins and cybersecurity, with a particular focus on their integrated applications in healthcare, fintech, Industry 4.0 and intelligent learning.

She currently serves as the conference chair for multiple international conferences (ICCCNet, ICDAM, SDCN), and has published over 30 high-quality papers in Q1 journals such as cloud computing, smart cities, and interactive learning environments, with a cumulative impact factor exceeding 75 and more than 280 citations. She not only leads multiple scientific research projects, but is also an active promoter of international research cooperation and is promoting the signing of several MoU cooperation agreements.

Proposed Technical Offerings

1. Edge Intelligence deployment Strategy:

Build a lightweight intelligent processing framework for edge devices to optimize resource utilization, which is suitable for smart factory, smart city and telemedicine scenarios.

2. Development and Optimization of Federated Learning Algorithms:

For data privacy-sensitive scenarios (such as medical data or financial data), a decentralized federated learning framework is provided, balancing efficiency and data sovereignty.

3. IoT Security Mechanism and Network Defense Scheme:

Blockchain-based authentication algorithms and intrusion detection systems (IDS) enhance the security of IoT devices in critical infrastructure such as smart grids or hospitals.

4. Design and Implementation of Digital Twin System

Establish a real-time mapping system between physical entities and digital models for remote monitoring of industrial manufacturing, smart homes or medical equipment.

5. Solution for data imbalance:

Design an enhancement mechanism for imbalanced datasets to improve the robustness and accuracy of machine learning systems under the federated architecture.

6. Promotion of cross-border scientific research cooperation and joint application for projects

It can assist in the contact for cooperation with research institutions in the UK and Europe, jointly apply for Horizon Europe, UKRI, IEEE conference cooperation or publication, etc.