Graph Machine Learning and Networks
Welcome to the Graph & Networks Research Group. We study graph theory, machine learning, and network science, with an interest in understanding and learning from the relationships in complex systems.
Our research connects theoretical questions about graphs with the development of learning methods and applications in biology and scientific discovery. The project is led by Dr. Sadia Sharmin.
Research
Our current research agenda focuses on three areas:
- Graph neural networks and large language models. Combining graph-structured data with text and images to improve reasoning in AI systems.
- Graph reconfiguration and algorithms. Studying how graph configurations can change under constraints, and developing algorithms for robust graph learning.
- Gene regulatory networks. Developing graph structure learning methods for network inference and gene perturbation analysis.
Research Project
Graph Machine Learning and Networks: Theory, Methods, and Applications
- Scheduled period
- January 2026 – December 2027
- Principal investigator
- Dr. Sadia Sharmin
- Program
- Improving Computer and Software Engineering Tertiary Education Project (ICSETEP), University Grants Commission of Bangladesh
This research brings together graph theory, graph machine learning, language models, and computational biology.