Research interests (in alphabetical order):

Applied probability, data science, statistical/machine learning, stochastic simulation, and system reliability.

You can see the complete list of publications here.

Research Projects:

• Advances in sequential Monte Carlo methods with applications to degradation data analysis: The majority of complex systems and products that empower our daily activities are subject to degradation. The latter affects the system lifetime, the quality of the service, and the corresponding safety of usage. Thus, a development of the corresponding reliability management and prognostic programs is of overwhelming importance. There exist numerous examples of the degradation phenomenon in various domains. For instance, degradation processes are common in ecology (forest degradation, water quality deterioration due to pollution, and fish population decay), manufacturing (a solar panel output deterioration, plane engines degradation, and corrosion), medicine (the shelf-life of drugs and vaccines), progression of chemical reactions, and many more. This project aims provide an important step forward in understanding and managing of general degradation processes. In particular, to develop a rigorous insight into the degradation phenomenon, one needs to address the problem of statistical inference in complex models and the problem of modeling extreme atypical rare-event scenarios. An efficient solution of these problems is the key to understanding of various failure mechanisms.


• Approximate computations in complex Bayesian models: theory and applications: Statistical inference is one of the most important tools used for scientific investigation. When dealing with data, the Bayesian paradigm is very appealing since it allows to incorporate prior knowledge into a proposed model, provides a well-structured inference method (conditional on the newly observed information), does not rely on asymptotic approximation, provides interpretable answers, and implements a straight-forward framework for model comparison and hypothesis testing. While these merits often come with high computational costs, continuing progress in the available computing resources allowed Bayesian statistics to rise to greater eminence in many scientific fields such as natural science, econometrics, social science, and engineering. However, despite recent advances, many real-life inference problems are still beyond the reach of classical Bayesian methods. Specifically, for many practical models, the evaluation of the likelihood function, a critical component of the Bayesian analysis, is either intractable or computationally prohibitive. In this project, you will investigate a number of methods such as the Pseudo-Marginal, the Integrated Nested Laplace, the Bayesian Synthetic Likelihood, the Variational Bayes, and the Approximate Bayesian Computation.


• Advances in sequential Monte Carlo methods: A series of interesting projects in the field of advanced Monte Carlo methods are available. In this project, you can expect to encounter various problems in the domains of Bayesian inference, time-series analysis, and modern machine learning.


• Advanced inference and machine learning with applications to crop yield: In this project, you will investigate a series of advanced statistical inference methods with application to crop yield. The methods range from time-series analysis and forecasting to artificial deep neural networks.


• Efficient methods for spatial micro-simulation: Spatial micro-simulation aims to generate a synthetic population from anonymous sample data at the individual level, which matches the observed population in a geographical zone for a given set of criteria in the most realistic manner. A good micro-simulation method will allow to the creation of estimated populations at a range of spatial scales where data may be otherwise unavailable. This project focuses on exploring efficient algorithms for spatial micro-simulation.

Some useful datasets:

• cars.csv
• mnist

Research Students:

• Daniel Herr (PhD. current), An integrative modelling approach to understanding human responses to hydrogen energy technologies. Joint Principal Advisor with Dr. Mitchell Scovell (CSIRO) .
• Hui (Alice) Yao (PhD. current), Infinite mixtures of phase type distributions: numerical approximation and efficient simulation. Joint Principal Advisor with Dr. Thomas Taimre.
• Robert Salamone (PhD. 2018), Advances in sequential Monte Carlo methods, University of Queensland. Associate Advisor with Prof. Dirk Kroese.
• Qibin Duan (PhD. 2018), Optimization by Rare-event Simulation, University of Queensland. Associate Advisor with Prof. Dirk Kroese.
• Rohan Shah (PhD. 2017), Monte Carlo Methods for Discrete Problems, University of Queensland. Associate Advisor with Prof. Dirk Kroese. Awarded 2017.
• Omar Alsibai (MSc. 2018), Monte Carlo Methods for Portfolio Credit Risk. Awarded 2017.