Kelly’s research focuses on Bayesian statistics, machine learning, dimension reduction techniques in Big Data, and time-varying parameter models. Kelly has a strong interest in developing new model specifications, and applying statistics models and machine learning algorithms in applied research areas such as bioinformatics, health and economics. Prior joining in JCU, Kelly worked as a Data Scientist at Data61, CSIRO and received a PhD degree at the University of Queensland in 2018.



-  "Emergency departmnet waiting time prediction in real-time"  funded by the Emergency Medicine Foundation, $36,733 https://emergencyfoundation.org.au/projects/emergency-department-waiting-time-predictions-in-real-time/


  • MA3832: Neural Network and Deep Learning (Level 3; CNS & TSV)
  • MA5832: Data Mining and Machine Learning (Level 5; CNS & ONL)
  • MA5852: Data Science Master Class 2 (Level 5; ONL & TSV)
  • Bayesian statistics
  • Dimension reduction techniques in Big Data
  • Machine learning algorithms
  • Biostatistics
  • Time-varying parameters
  • Macroeconomics
  • 2017 to 2019 - Data Scientist, Data61,CSIRO (Brisbane)
  • 2013 to 2017 - UQ Scholarship for Post Graduate Students
  • 2013 to 2014 - Distinguished Teaching Awards
  • 2011 to 2012 - UQ Summer Research Scholarship
  • 2009 to 2011 - UQ Scholarship for Undergraduate Students

These are the most recent publications associated with this author. To see a detailed profile of all publications stored at JCU, visit ResearchOnline@JCU. Hover over Altmetrics badges to see social impact.

Other research outputs
Current Funding

Current and recent Research Funding to JCU is shown by funding source and project.

Emergency Medicine Foundation - Rural and Remote Grant

ED waiting time predictions in real-time: development of data acquisition system and performance evaluation of advanced statistical models.

Indicative Funding
$36,733 over 1 year (administered by Metro South Hospital and Health Service)
Emergency department (ED) waiting times are a significant predictor of the patient experience. This project aims to use advanced statistical models and machine-learning algorithms to capture dynamic fluctuations in waiting time, to implement and validate the prediction performance of these models. A solution that is capable of sourcing data from ED information systems and feed it into prediction models to generate waiting time forecasts would bring practical benefits for staff and patients. There is also potential to assist clinicians and nurses to estimate demand for care and calibrate workflow.
Andrew Staib, Anton Pak and Kelly Trinh in collaboration with Rob Eley and Brenda Gannon (Metro South Hospital and Health Service, Australian Institute of Tropical Health & Medicine, College of Science & Engineering and The University of Queensland)
Waiting time prediction; Data acquisition system; Machine learning; Emergency department

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