2021 Distinguished Lecture Series in Statistical Sciences: Kerrie Mengersen
Kerrie Mengersen is a Distinguished Professor of Statistics at the Queensland University of Technology in Brisbane, Australia. She is a Deputy Director of the Australian Research Council Centre of Excellence in Mathematical and Statistical Frontiers, an ARC Laureate Fellow, and the Director of the QUT Centre for Data Science. Kerrie’s research interests focus on the development of (mostly Bayesian) models and computational algorithms, and their application to substantive challenges in health, the environment, and industry.
Hourly Schedule
Monday, September 20, 2021
- 4:00 PM - 5:00 PM
- Bayesian Modelling and Analysis of Challenging Data: Making New Sources of Data Trustworthy
- Evidence-based decisions depend critically on trustworthy data. Two forms of data that have been brought into question in recent times are sensor data and citizen science data. Sensors are a key component of IoT and have created a step-change in our ability to monitor systems. However, they are often subject to technical anomalies that raise concerns about the validity of their data and signals. Citizen science is also growing in utility and interest in many areas, but often suffers from concerns about the credibility the information provided by community members. In this presentation, I will describe some new approaches to resolving some of these concerns. These include new methods for anomaly detection in high-dimensional streaming time series, and Bayesian models for estimating the latent ability of citizens taking into account the difficulty of the tasks. This work has been developed in collaboration with a number of teams working on challenges in ecology and industry; these teams will be acknowledged and the associated challenges discussed during the presentation.
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Speakers:
Kerrie Mengersen
Tuesday, September 21, 2021
- 4:00 PM - 5:00 PM
- Bayesian Modelling and Analysis of Challenging Data: Identifying the Intrinsic Dimension of High-Dimensional Data
- One of the challenges of high-dimensional data is identifying the true information contained therein. In this presentation, I will describe some approaches that we have been considering to address this challenge. These approaches include Bayesian methods of matrix factorisation, intrinsic dimension and structured variable selection. Our work on these methods is set in the context of substantive case studies in image analysis, sport and genomics.
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Speakers:
Kerrie Mengersen
Speaker
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Kerrie MengersenDistinguished Professor of Statistics at the Queensland University of Technology in Brisbane; Deputy Director of the Australian Research Council Centre of Excellence in Mathematical and Statistical Frontiers; Director of the QUT Centre for Data Science
Kerrie Mengersen is a Distinguished Professor of Statistics at the Queensland University of Technology in Brisbane, Australia. She is a Deputy Director of the Australian Research Council Centre of Excellence in Mathematical and Statistical Frontiers, an ARC Laureate Fellow, and the Director of the QUT Centre for Data Science. Kerrie’s research interests focus on the development of (mostly Bayesian) models and computational algorithms, and their application to substantive challenges in health, the environment and industry.