View Proposal

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Proposer

Yingfang Yuan

Title

Empowering Scientific Research with Graph Neural Networks and Real-World Applications

Goal

The primary objective of this project is to use graph neural networks (GNNs) as a powerful tool to enhance multidisciplinary research and drive impactful, real-world applications.

Description

This project centers on Graph Neural Networks (GNNs) and their applications across a range of disciplines. While the primary focus is on developing and implementing GNNs, specific research problems are flexible and can be tailored to each student’s interests and academic background. Students have the opportunity to explore diverse applications of GNNs, from uncovering complex patterns in financial data to enhancing understanding in biological networks, social sciences, and beyond. If you have any questions or ideas, please feel free to reach out via email.

Resources

Background

Url

Difficulty Level

High

Ethical Approval

None

Number Of Students

3

Supervisor

Yingfang Yuan

Keywords

machine learning, deep learning, graph neural networks

Degrees

Master of Engineering in Software Engineering
Master of Design in Games Design and Development
Master of Science in Artificial Intelligence
Master of Science in Artificial Intelligence with SMI
Master of Science in Business Information Management
Master of Science in Computer Science for Cyber Security
Master of Science in Computer Systems Management
Master of Science in Computing (2 Years)
Master of Science in Data Science
Master of Science in Human Robot Interaction
Master of Science in Information Technology (Business)
Master of Science in Information Technology (Software Systems)
Master of Science in Network Security
Master of Science in Robotics
Master of Science in Software Engineering