Ubaid Azam

PhD researcher in trustworthy machine learning for low-resource and safety-critical domains.

University of Southampton, United Kingdom

Portrait of Ubaid Azam

About

I am a final-year PhD researcher in Computer Science at the University of Southampton, working on trustworthy machine learning for low-resource and safety-critical domains: Bayesian uncertainty quantification, explainability, and privacy-preserving deployment.

My research asks how models can know what they do not know, explain their reasoning, and be deployed responsibly where data is scarce and mistakes are costly. I am the primary researcher on a competitively awarded Alan Turing Institute grant (PICASO), contributed to the United Nations AI Trust and Safety Re-Imagination Programme, and won two University of Southampton Doctoral College Director's Awards. I am currently on the job market for research scientist and applied scientist roles in industry or academia.

Research

Uncertainty & explainability

Bayesian deep Gaussian Processes that let models express calibrated confidence and explain their predictions in low-resource, high-stakes settings such as medicine and content moderation.

Privacy-preserving deployment

Federated prompt tuning (FLiP) that adapts language models across languages without centralising sensitive data, keeping trustworthy AI practical where data cannot be shared.

Knowledge representation (PICASO)

Probabilistic conceptual spaces that model vagueness, ambiguity, and confidence, improving how AI systems make sense of the world under uncertainty.

News

Publications