Associate Professor at University of Sydney, Australia
Dr. Clara Grazian is an Associate Professor at the School of Mathematics and Statistics, University of Sydney, specializing in Bayesian statistics, computational methods, and their applications in health, environmental, and material sciences. She has held academic and research positions across prestigious institutions in Australia, the UK, France, and Italy.
š¹Professional Profile:
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šEducation Background
Dr. Grazian earned her Joint Ph.D. in Applied Mathematics and Statistics from CEREMADE UniversitĆ© Paris-Dauphine (France) and the Department of Statistics, Sapienza UniversitĆ di Roma (Italy), graduating Excellent cum laude in 2016. She also holds a Masterās degree in Statistics (110/110 cum laude) from Sapienza and a Master 2 in Mathematical Modelling and Decision from UniversitĆ© Paris-Dauphine (Mention trĆØs bien). Her foundational degree is a Bachelor in Statistical Sciences from UniversitĆ degli Studi di Torino (110/110 cum laude).
š¼ Professional Development
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2025āPresent: Associate Professor, University of Sydney
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2022ā2024: Senior Lecturer, University of Sydney
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2019ā2022: Senior Lecturer, University of New South Wales
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2018ā2019: Research Fellow, UniversitĆ āG. dāAnnunzioā, Italy
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2017ā2019: Postdoctoral Scientist, Big Data Institute & Nuffield Department of Medicine, University of Oxford
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2015ā2016: Research Fellow, Sapienza UniversitĆ di Roma
Dr. Grazian has also contributed significantly to cross-disciplinary projects in genomics, epidemiology, and materials science.
š¬Research Focus
Her research focuses on Bayesian inference, model selection, copula models, approximate Bayesian computation (ABC), posterior approximations, and machine learning applications in fields like tuberculosis resistance prediction, urban dynamics, and nanomaterials discovery. She is also active in developing computational tools for likelihood-free inference and experimental design.
šAuthor Metrics:
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Numerous peer-reviewed publications in high-impact journals.
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Supervised several Ph.D., Honours, and Postdoctoral researchers across fields including biostatistics, data science, and computational modelling.
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Developer of widely-used statistical software packages such as DARWIN, Minos, PETabc, and BayesMIC.
šAwards and Honors:
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University of Sydney Postgraduate Award (2024)
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J.B. Douglas Postgraduate Award, SSA (2024)
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Mike Tallis PhD Award (2024) ā Multiple recipients under her supervision
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Invited Speaker at major conferences including ISBA World Meeting 2024 and seminars hosted by the Statistical Society of Australia
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Supervised Tong Xie, recipient of top YouTube video recognition by the DARE ARC Centre and selected for prestigious global computing programs.
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2024 SIDRA SOLUTIONS Postgraduate Award (supervisor of award-winning thesis in urban transport planning)
šPublication Top Notes
1. Assessing the Invertibility of Deep Biometric Representations: Investigating CNN Hyperparameters for Enhanced Security Against Adversarial Attacks
Authors: C. Grazian, Q. Jin, G. Tangari
Published in: Expert Systems with Applications, Volume 264, 2025, Article 125848
Summary:
This paper investigates the security vulnerabilities in deep biometric systems by evaluating the invertibility of biometric feature representations derived from Convolutional Neural Networks (CNNs). The authors systematically analyze how different CNN hyperparameters affect the robustness of these models against adversarial inversion attacks. The work proposes tuning strategies to improve security without compromising biometric performance.
Contribution: Enhances understanding of CNN-based biometric security, a crucial area in identity verification systems.
Relevance: AI security, adversarial robustness, biometrics.
2. Darwin 1.5: Large Language Models as Materials Science Adapted Learners
Authors: T. Xie, Y. Wan, Y. Liu, Y. Zeng, S. Wang, W. Zhang, C. Grazian, C. Kit, et al.
Published in: arXiv preprint arXiv:2412.11970, 2024
Summary:
This work introduces Darwin 1.5, a tailored version of large language models (LLMs) specifically adapted for materials science learning tasks. The model is fine-tuned on scientific texts and datasets related to materials discovery, showcasing improvements in knowledge retrieval, data interpretation, and hypothesis generation.
Contribution: Dr. Grazian contributed Bayesian modeling insights to the model evaluation metrics.
Relevance: Interdisciplinary AI application, materials informatics, LLM adaptation.
3. Approximate Bayesian Computation with Statistical Distances for Model Selection
Authors: C. Angelopoulos, C. Grazian
Published in: arXiv preprint arXiv:2410.21603, 2024
Summary:
The paper explores model selection under Approximate Bayesian Computation (ABC) by incorporating robust statistical distance measures (e.g., Wasserstein, Energy Distance). The approach helps mitigate issues in likelihood-free inference where traditional ABC may struggle with model choice accuracy.
Contribution: Dr. Grazian co-developed the methodological framework and designed experiments for evaluating model selection efficacy.
Relevance: Computational statistics, Bayesian inference, ABC methods.
4. Parametric Maps of Kinetic Heterogeneity and Ki in Dynamic Total Body PET using Approximate Bayesian Computation
Authors: Q. Gu, G. Angelis, D. Bailey, P. Roach, C. Grazian, G. Emvalomenos, et al.
Presented at: 2024 IEEE Nuclear Science Symposium (NSS) and Medical Imaging Conference (MIC)
Summary:
This paper applies ABC methods to generate parametric maps from dynamic total-body PET scans, providing estimates for kinetic heterogeneity and Ki (influx rate constant). The approach addresses complex likelihoods in dynamic PET data.
Contribution: Dr. Grazian contributed the statistical modeling and implementation of the ABC framework.
Relevance: Medical imaging, Bayesian computation, PET quantification.
5. Novel Bayesian Algorithms for ARFIMA Long-Memory Processes: A Comparison Between MCMC and ABC Approaches
Authors: J.C. Gabor, C. Grazian
Published in: arXiv preprint arXiv:2410.13261, 2024
Summary:
The study compares traditional MCMC techniques and ABC for estimating parameters of ARFIMA (Autoregressive Fractionally Integrated Moving Average) processes, which model long-range dependencies in time series. The paper highlights the efficiency and trade-offs of both approaches in complex likelihood environments.
Contribution: Dr. Grazian led the design of the ABC-based inference strategy and performance benchmarking.
Relevance: Time series analysis, long-memory processes, Bayesian methodology.
.Conclusion:
Dr. Clara Grazian is an exceptionally strong candidate for the Best Researcher Award, distinguished by her deep theoretical expertise, cross-disciplinary innovation, impactful mentorship, and software development. Her work is both methodologically sophisticated and societally relevant.
Recommendation: Strongly support her nomination. With a growing global presence and continued translation of her research into practice, Dr. Grazian exemplifies the qualities of a 21st-century thought leader in statistics and data science.