Research Interests

I work across probabilistic AI, statistics, and signal processing. My research focuses on developing scalable, uncertainty-aware inference methods, with applications to complex sequential and multi-agent systems.

• Predictive and post-Bayes inference: martingale posteriors, predictive resampling, and generalised Bayes methods for robust uncertainty quantification under model misspecification.
• Nonparametric Bayesian methods: Bayesian clustering and analysis for high-dimensional data, including biomedical applications such as RNA sequencing.
• Scalable and distributed inference: variational inference, sequential Monte Carlo, and MCMC for high-dimensional latent-variable models.
• Sensor fusion and multi-object tracking: decentralised optimisation and data fusion of heterogeneous sensors; robust tracking under clutter and occlusion; group interaction modelling and intent inference in multi-agent systems.

I actively seek interdisciplinary collaborations, particularly in applications such as animal interaction and behaviour learning, pollution tracking, and marine monitoring.