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
Meta-learning framework enabling rapid fine-tuning of large foundation models to new health and agricultural tasks with as few as 5–10 labelled examples per class.…
Active · 2024–2026FairML: Algorithmic Fairness Auditing Suite
Toolchain for automated bias detection, causal attribution, and counterfactual explanation generation for ML models deployed in high-stakes African institutional contexts.…
Active · 2025–2027Related Publications
Multi-Modal Sensor Fusion for Robust Agent Tracking
David Park, Thomas Mueller
International Journal of Sensor Networks
Causal Fairness Auditing in High-Stakes AI Systems
Anya Chen, Justice Kwame Appati
Advances in Neural Information Processing Systems
Interested in Collaborating?
We welcome partnerships with institutions, NGOs, and researchers working at the frontier of AI and this domain.