In the machine learning theme, AberdeenML, we tackle problems related to the School's and University's strategic focus of Data and Artificial Intelligence (AI). We have 12 core faculty members and growing, as well as more than 15 PhD students, with our research foci ranging from theory to the applications of Machine Learning.
Application domains include energy, agri-food technology, and healthcare, while more theoretical research encompasses human/machine collaboration, natural language technologies and explanation, as well as machine/deep learning research itself.
Much of the research is interdisciplinary in nature, covering areas such as energy transition and decommissioning, nuclear reactors, bias of AI systems, industrial applications, psycholinguistics, education, and healthcare.
AberdeenML's areas of interest and expertise cover a wide range of topics including:
- Data Science
- Deep Learning Architectures such as Capsule Neural Networks and Transformers
- Self-Supervised Learning
- Computer Vision
- Causal Inference
- Multi-Objective Optimisation
- Robotics
- Natural Language Processing
- Decision Support Systems and Autonomous Systems
Meetings
Our group meets regularly:
- Machine Learning Reading Group: Thursdays from 12:00-13:00 either online or in Meston Common room. This meeting typically consists of discussion of recent research papers, presentations of ongoing work from students and staff, or technical tutorials.
- ML Theme Catchup: Fridays from 13:00-14:00 either online or in Meston. This is a chance for an informal catchup between theme members to discuss ongoing research over lunch/coffee.