Researcher

Dr Clara Grazian

My Expertise

  • Bayesian statistics
  • Statistics for genomics
  • Effect of air pollution on environment and human health
  • Effect of human activities on animal behaviours

Keywords

Fields of Research (FoR)

Statistics, Applied Statistics, Biostatistics

Biography

Clara Grazian received a joint PhD in 2016 from Université Paris-Dauphine under the supervision of Prof. Christian Robert and from Sapienza Università di Roma under the supervision of Prof. Brunero Liseo.

Her research interests include: Bayesian statistics, mixture models, spatio-temporal modelling and copula models and variable selection, with applications in climatology, epidemiology, psychology and genetics.

Before joining UNSW, she was...view more

Clara Grazian received a joint PhD in 2016 from Université Paris-Dauphine under the supervision of Prof. Christian Robert and from Sapienza Università di Roma under the supervision of Prof. Brunero Liseo.

Her research interests include: Bayesian statistics, mixture models, spatio-temporal modelling and copula models and variable selection, with applications in climatology, epidemiology, psychology and genetics.

Before joining UNSW, she was Postdoctoral Fellow at the University of Oxford, working on understanding genomic mechanisms conferring resistance to tuberculosis and at the Università degli Studi "Gabriele d'Annunzio" working on understanding the effect of pollution on human health.
 


My Grants

January 2021 - June 2021: ACEMS Research Support Scheme (RSS), Research Project: "Exploiting composite likelihoods for likelihood-free problems." (14,588.32$), in collaboration with QUT. 

October 2020 - March 2021: ACEMS Industry Collaboration Support Scheme (ICSS), Research Project: "Developing Bayesian methods for modelling the dynamics of complex systems in sports: perfomance vs injuries" (20,000$), in collaboration with QUT. 

October 2020 - September 2021: Defence Innovation Network Scheme, Research Project: "Optimising ADF military working dog performance through next-generation monitoring systems" (175,000$), in collaboration with University of Sydney and Western Sydney University.

July 2020 - June 2021: Defence Science Partnering Deed - Schedule Research Agreement (Defence Science & Technology Group - DSTG), Research Project: "Knowledge Sythesis for Autonomous Analyst" (160,000$), in collaboration with UTS and University of Wollongong.

 

 

 


My Qualifications

PhD in Mathematical Statistics (2016) - Université Paris-Dauphine and Sapienza Università di Roma

Master 2 in Mathematical Statistics (2011) - Université Paris-Dauphine

Master in Statistical Sciences (2012) - Sapienza Università di Roma

Bachelor in Statistical Sciences (2012) - Università degli Studi di Torino


My Awards

Here are the received grants since 2015

2018 - Research Project of National Interest (Italian Ministry of Research and Education) - "Environmental processes and human activities: capturing their interactions via statistical methods", (principal investigator: Daniela Cocchi)

2017 - International Exchanges Scheme, The Royal Society - "High-dimensional Bayesian dependence modelling with conditional copulas" (with Luciana Dalla Valle, Julian Stander and Brunero Liseo)

2016 - Research Project of National Interest (Italian Ministry of Research and Education) - "Likelihood-free methods for inference" (principal investigator: Brunero Liseo)

2015 - International Exchanges Scheme, The Royal Society - "Empirical and Bootstrap Likelihood procedures for Approximate Bayesian Inference" (Fabrizio Leisen and Brunero Liseo)

 

 


My Research Activities

Dr Grazian is interested in Bayesian analysis for complex systems, in particular for clustering and dependence modelling. Her research is focused on developing consistent techniques for Bayesian clustering in several situations (independent data, temporal data, spatial data) and evaluating dependence functionals in a semi-parametric setting. She works in several applied settings, in particular in genomics, finance and environmental sciences:

  • clustering approaches to identify several levels of resistance in M. Tuberculosis
  • spatial clustering for environmental variables
  • temporal clustering to model animal behaviour
  • functionals of dependence for log-returns 
  • conditional functionals of dependence for building materials

My Research Supervision


Areas of supervision

  • Mixture models
  • Copula models
  • Bayesian spatial modelling
  • Clustering
  • Spatio-temporal modelling
  • Applications in genomics, cyber-security, ecology, finance

Examples of project

1.  Data-driven approaches for determining the rate of somatic mutation in individual cells

In collaboration with the Garvan Institute of Medical Research (Dr. Manu Singh, Prof. Tim Peters, A/Prof. Fabio Luciani): 

We aim to estimate the rate of somatic mutation along a clonal evolutionary tree from single-cell genomic sequencing. The project is analogous to estimating the value of parameter K from a Dirichlet multinomial distribution (i.e. estimating the total number of unique ball colours in a Polya urn), with two added variables: i) The urn draws are not true replicates in that each one is situated in phylogenetic space along an evolutionary tree, and ii) there is assumed to be a degree of observational error in the draws, incurred by incorrect genotyping from the sequencer.

The candidate will be expected to deploy and test a number of existing computational approaches, with the potential of developing novel algorithms and publication in high-impact journals. They will have a strong background in computer science and/or software engineering, and be comfortable operating in Linux and high-performance computing environments. A background in mathematics and/or statistics, especially probability distributions, will be looked upon favourably.

2. Characterising rainfall data and PM10 pollution data through spatio-temporal modelling

We propose a new model for interpreting the environmental data as a mixture of characteristic patterns, that we interpret as different clusters. The probability that an area is clustered specific group, at each time instant, is non-parametrically estimated using the Logistic-Gaussian process. Owing to a new formalization and the way we specify the coregionalization matrix of the associated multivariate Gaussian process, our model is invariant with respect to the choice of the reference element and of the ordering of the probability vector components. We fit the model under a Bayesian framework, and show that the Markov chain Monte Carlo algorithm we propose is straightforward to implement.
We perform a simulation study with the aim of showing the ability of the estimation procedure to retrieve the model parameters. We also test the performance of the information criterion we used to select the number of groups.

3. Approximate Bayesian computation for kinetic models 

In collaboration with the Sydney School of Health Science

Dynamic Positron emission tomography (PET) is a non-invasive tomographic imaging technique that produces quantitative 3D maps of physiological parameters. The mapping from the images to the physiological parameters usually requires time-series analysis of a dynamic sequence of images using kinetic models and optimisation algorithm. We use a Bayesian approximate method to derive posterior distributions of important parameters which can be implemented in high-dimensional images. 

4. Detecting maritime vessel anomalies using k-means clustering

In collaboration with Dr Shane Keating and Spiral Blue (Sydney based space industry company that uses AI techniques for Earth observation and satellite intelligence)

Monitoring maritime vessel traffic is critical to trade and security. Machine learning techniques have automated the task of locating vessels in satellite images. Given the location of vessels, what other insights can be extracted from this information? If we perform k-means clustering, we can group vessels by location, time spent in the region or other possible metrics. Analysis of patterns formed by groupings may reveal useful insights. For example, could we have detected an anomaly in the Suez Canal, when it was blocked by the "Ever Given" container ship?

 

 


Currently supervising

Current Supervisions

  • Hanwen Xuan (PhD): Copula representation of the Black and Litterman model for portfolio optimisation
  • Tong Xie (PhD)
  • Maryam Bostanara (PhD)
  • Adam Stanley (Honours)
  • James Gabor (Honours): Analysis of long-memory financial sequences
  • Shanshui Gu (Honours)
  • Vasiliki Vamvaka (Master)

Past Supervisions: 

  • PhD Students
  • Diego Battagliese (PhD in Mathematical Methods for Economics and Finance, Sapienza University of Rome - 2020): "Penalising Model Complexity" (joint with Prof. B. Liseo)
  • Honours Students
  • Louis Ye (Honours programme in Statistics - 2020): "Temporal extensions on spatial modelling with Dirichlet processes"
  • Master Students
  • Chenhao Yue (Master of Financial Mathematics - 2021): "Approximate Bayesian computation for long memory processes"
  • Tingyi Lee (Master of Statistics - 2021): "New formulation of the Dirichlet process on clustering"
  • Xujing Quan (Master of Statistics - 2021): "Portfolio risk management based on the skewed Student t distribution and GARCH models"
  • Yuan Gong (Master of Statistics - 2020): "A time clustering model for temporal data"
  • Tong Wang (Master of Statistics - 2020): "A copula representation of the asymmetric Laplace distribution"
  • Yiwei Wang (Master of Statistics - 2020): "The invertibility of biometric representations through CNN"
  • Taiming Xu (Master of Financial Mathematics - 2020): "Copula representation of the Black and Litterman model" 
  • Xinyu Guan (Master of Statistics - 2019): "Evaluating M. Tuberculosis resistance through mixture models"
  • Wei Li (Master of Statistics, University of Oxford - 2018): "Establishing antimicrobial resistance breakpoints via mixture models"
  • Ilaria Masiani (Master of Statistics, University Paris-Dauphine - 2014): "On proper scoring rules for Bayesian model selection" (joint with Prof. C. P. Robert)
  • Undergraduate Students
  • Alex McInnes (Bachelor in Science - 2019): "An application of copulas to OPEC's changing influence on fossil fuel prices"

My Teaching

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