Associate ProfessorClara Grazian

A/Prof Grazian's research spans Bayesian statistics, machine learning, and statistical methodologies for environmental and biological sciences, with a strong focus on addressing challenges in uncertainty quantification, high-dimensional inference, and data-driven decision-making. Major problems she aims to tackle include improving the interpretability and generalization of AI models, developing robust statistical frameworks for environmental risk assessment, and enhancing the security of biometric representations. Her theoretical contributions include advancing Bayesian mixture models and copula models, proposing novel MCMC and ABC methods for long-memory processes, and integrating deep learning with statistical modelling, in particular in areas such as material science and microbiology.

A/Prof Grazian aims to develop scalable Bayesian models that integrate mechanistic and data-driven approaches for environmental monitoring, explore foundational models, and refine statistical methodologies for high-resolution genomic and biometric data. Her research is inherently interdisciplinary, drawing from statistics, AI, remote sensing, and genomics, and involves collaborations with experts at institutions such as the DARE Research Hub, leading Australian universities, and international networks in Bayesian statistics and AI. These collaborations facilitate cross-disciplinary advancements and ensure the practical impact of my work in scientific and applied domains.

A/Prof Grazian's research aligns with the Faculty of Science Research Strengths - Understanding the Universe, Fundamental Laws of Nature, Complex Systems, Precision and Digital Health, Data and Decisions, National Security .