Queen's University Belfast

Robust machine learning for imperfect information.

We study machine learning and computer vision systems that remain reliable when supervision, data, evaluators, or deployment conditions are noisy, incomplete, or adversarial.

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Abstract network visualization of clean and noisy information streams

Research Focus

QUB ML Lab works on robust machine learning, AI safety, computer vision, and foundation models when data, labels, evaluators, or deployment conditions are imperfect.

Imperfect Supervision

Noisy labels, open-set noise, coarse labels, and reliable representation learning from imperfect visual data.

AI Safety and Evaluation

Certification, adversarial robustness, and statistically reliable evaluation for models and LLM-based systems.

Foundation Models

Robust visual and multimodal learning with CLIP-style representations, healthcare AI, and domain-shifted data.

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