Machine Learning Foundations
Fundamental research in deep learning architectures, reinforcement learning, and the mathematical foundations of intelligence — pushing the theoretical boundaries of AI.
Machine Learning Foundations
Our machine learning foundations group advances the theoretical and practical understanding of deep learning, reinforcement learning, and neural architecture design. From meta-learning and continual learning to federated systems and causal inference, we tackle the open problems that underpin real-world AI deployment.
Key Research Topics
Deep Learning Architecture
Neural architecture search, attention mechanisms, and scalable training strategies for large-scale deep learning models.
Reinforcement Learning
Deep RL algorithms for sequential decision-making in partially observable, multi-agent, and non-stationary environments.
Federated & Distributed AI
Privacy-preserving distributed learning frameworks that train models across decentralised nodes without raw data sharing.
Explainability & Fairness
Interpretable model design, algorithmic fairness auditing, and causal attribution methods for high-stakes AI applications.
Current Projects
MetaAdapt: Cross-Domain Few-Shot Learning
Cross-domain few-shot learning framework that rapidly adapts AI models to new tasks with minimal labelled data.
FairML: Algorithmic Fairness Auditing Suite
Algorithmic fairness auditing suite that detects and mitigates bias in machine learning systems across sensitive domains.
Related Publications
Interested in Collaborating?
We welcome partnerships with institutions, NGOs, and researchers working at the frontier of AI and this domain.