Slava Vaisman

Available projects

I am always looking for prospective Ph.D. students. If you wish to know more about available projects, feel free to send me an email with your CV and a few lines regarding your research background and interests.

For details, please see: https://people.smp.uq.edu.au/RadislavVaisman/Research.html

Advances in Sequential Monte Carlo Methods with Applications to Degradation Data Analysis (PhD)

The majority of complex systems and products that empower our daily activities are subject to degradation. This affects the system lifetime, the quality of the service, and the corresponding safety of usage. Thus, a development of reliability management and prognostic programs is of overwhelming importance. In this project, you will investigate methods for understanding and managing of degradation processes. Specifically, the broad objective of this project is to develop new mathematical techniques and fast computational algorithms for inference in complex statistical models by building on recent advances in Monte Carlo methods, stochastic optimisation, and rare-event sampling techniques.

Approximate Computations in Complex Bayesian Models: Theory and Applications (PhD)

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, a 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 for 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 (Honours/Phd)

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