Hoa Bui was an invited speaker of the Continuous Optimisation cluster at the XL National Congress of Statistics and Operations Research and the XIV Conference on Public Statistics from 7 to 10 November 2023 in Elche, Spain. She shared her recent research on "Single-projection procedure for solving convex optimisation problems in Hilbert space." She also gave two seminal talks at the Universität der Bundeswehr München (Munich, Germany) and the University of Alicante (Alicante, Spain).
The AustMS WIMSIG Cheryl E. Praeger Travel Award funded her visit to the University of Alicante to collaborate with Professor Marco Lopez and Professor Abderrahim Hantoute, world-renowned experts in semi-infinite optimisation. They are collaborating to extend Hoa's recent results on single projection procedure to semi-infinite programming.
Hoa at the conference: XL National Congress of Statistics and Operations Research and the XIV Conference on Public Statistics
Events
Researchers Catch-Up
Sandy Spiers: Understanding the Difficulties of Optimisation
Within the architecture of an optimisation solution, the algorithmic component is often misunderstood or neglected, yet this is where most of the heavy lifting is done.
Sandy presented that a way to remedy this by unpacking what actually makes an optimisation problem difficult. He explored the idea of decision dimensions and explained why some problems are far more complex than others.
By demystifying some common optimisation algorithms, Sandy showed how practical assumptions lead to more intuitive algorithms, providing a more holistic understanding of optimisation solutions.
Gabriel Gonzalez: Bayesian Hierarchical Models for Estimating Individual Failure Time Distributions from Inspection Data with Noise
In maintenance and reliability engineering, assessing the performance and failure characteristics of systems is a critical task. This assessment often involves analysing individual failure time distributions obtained from inspection data. However, real-world inspection data is frequently contaminated with noise, leading to conclusions that may not be justifiable. Gabriel compared two Bayesian hierarchical approaches he has been investigating that address the challenge of estimating sampling and measurement noise.
The Bayesian hierarchical models that Gabriel has devised explicitly account for the noise in the inspection data. Doing so enables a more robust and accurate comparison of these distributions. The hierarchical nature of the approach allows for the incorporation of prior information and the borrowing of strength across different units or components, making it particularly useful when dealing with limited sample sizes or sparse data.
Gabriel illustrated the effectiveness of his method through a case study. The results demonstrate the ability to enhance the reliability and precision of comparisons between individual failure time distributions in the presence of noisy inspection data, thereby providing valuable insights for decision-making and quality improvement.
Stay tuned for our next issue where we will cover:
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This month's newsletter focuses on the activities in November and includes an update on the travels of Hoa and Eden
IN THE SPOTLIGHT
Congratulations to Research Fellow Eden Li
TEAM NEWS
Welcome to two new members of the Strategic Risk and Reporting Board: Peter Rose , representing Alcoa, and Johan Breytenbach , representing Roy Hill.
INTERNATIONAL COLLABORATIONS
Hoa Bui was an invited speaker of the Continuous Optimisation cluster at the XL National Congress of Statistics and Operations Research and the XIV Conference on Public Statistics from 7 to 10 November 2023 in Elche, Spain. She shared her recent research on "Single-projection procedure for solving convex optimisation problems in Hilbert space." She also gave two seminal talks at the Universität der Bundeswehr München (Munich, Germany) and the University of Alicante (Alicante, Spain).
The AustMS WIMSIG Cheryl E. Praeger Travel Award funded her visit to the University of Alicante to collaborate with Professor Marco Lopez and Professor Abderrahim Hantoute, world-renowned experts in semi-infinite optimisation. They are collaborating to extend Hoa's recent results on single projection procedure to semi-infinite programming.
Hoa at the conference: XL National Congress of Statistics and Operations Research and the XIV Conference on Public Statistics
Events
Researchers Catch-Up
Sandy Spiers : Understanding the Difficulties of Optimisation
Within the architecture of an optimisation solution, the algorithmic component is often misunderstood or neglected, yet this is where most of the heavy lifting is done.
Sandy presented that a way to remedy this by unpacking what actually makes an optimisation problem difficult. He explored the idea of decision dimensions and explained why some problems are far more complex than others.
By demystifying some common optimisation algorithms, Sandy showed how practical assumptions lead to more intuitive algorithms, providing a more holistic understanding of optimisation solutions.
Gabriel Gonzalez : Bayesian Hierarchical Models for Estimating Individual Failure Time Distributions from Inspection Data with Noise
In maintenance and reliability engineering, assessing the performance and failure characteristics of systems is a critical task. This assessment often involves analysing individual failure time distributions obtained from inspection data. However, real-world inspection data is frequently contaminated with noise, leading to conclusions that may not be justifiable. Gabriel compared two Bayesian hierarchical approaches he has been investigating that address the challenge of estimating sampling and measurement noise.
The Bayesian hierarchical models that Gabriel has devised explicitly account for the noise in the inspection data. Doing so enables a more robust and accurate comparison of these distributions. The hierarchical nature of the approach allows for the incorporation of prior information and the borrowing of strength across different units or components, making it particularly useful when dealing with limited sample sizes or sparse data.
Gabriel illustrated the effectiveness of his method through a case study. The results demonstrate the ability to enhance the reliability and precision of comparisons between individual failure time distributions in the presence of noisy inspection data, thereby providing valuable insights for decision-making and quality improvement.
Stay tuned for our next issue where we will cover:
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