My research focuses on Bayesian statistics, machine learning, dimension reduction techniques, and time-varying parameter models. I have a strong interest in developing methodology statistical analysis and machine learning algorithms in applied research areas.

Methodology research:

- Bayesian time series analysis

- Machine learning and neural network in time series

- High dimensional modelling

- Modelling uncertainties

- Spatial analysis and point pattern analysis

 

Applied research 

- Macroeconomic (forecasting and understanding impacts of economic uncertainities, policies on an economy)

- Economic Health

- Economic geography

- Health application  

 

Grants: 

-  "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/

 

Teaching

• MA2405: Advanced Statistical Modelling (Level 2; CNS & TSV)

Interests

Research

• Bayesian statistics
• Dimension reduction techniques
• State Space Models
• Time-series
• Forecasting
• Machine learning algorithms
• Bioinformatics
• Macroeconomics

Experience

• 2017 to 2019 - Data Scientist, Data61,CSIRO (Brisbane)

Research Disciplines

• Health services and systems (4203)
• Econometrics (3802)
• Machine learning (4611)

Socio-Economic Objectives

• Provision of health and support services (2003)
• Clinical health (2001)
• Macroeconomics (1502)

Awards

• 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.

Journal Articles

• Trinh K, Staib A and Pak A (2023) Forecasting emergency department waiting time using a state space representation. Statistics in Medicine, 42. pp. 4458-4483
• Vos G, Trinh K, Sarnyai Z and Rahimi Azghadi M (2023) Ensemble machine learning model trained on a new synthesized dataset generalizes well for stress prediction using wearable devices. Journal of Biomedical Informatics, 148.
• Vos G, Trinh K, Sarnyai Z and Rahimi Azghadi M (2023) Generalizable machine learning for stress monitoring from wearable devices: A systematic literature review. International Journal of Medical Informatics, 173.
• Blasdell K, Morand S, Laurance S, Doggett S, Hahs A, Trinh K, Perera D and Firth C (2022) Rats and the city: Implications of urbanization on zoonotic disease risk in Southeast Asia. Proceedings of the National Academy of Sciences of the United States of America, 119 (39).
• Cross J, Huo C and Trinh K (2021) Returns, volatility and the cryptocurrency bubble of 2017–18. Economic Modelling, 104.

Other research outputs

• Trinh K and Cameron L (2017) Four factors driving the price Bitcoin. The Conversation,

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

Advisory Accreditation: I can be on your Advisory Panel as a Secondary Advisor.

These Higher Degree Research projects are either current or by students who have completed their studies within the past 5 years at JCU. Linked titles show theses available within ResearchOnline@JCU.

Current

• The development of a computational model using data science approaches to predict frailty and health deficits in the elderly living in institutional care to better support the aged care system. (PhD , External Advisor)

Completed

• Machine Learning for Stress Measurement using Wearable Sensor Biomarkers (2023, Masters , Secondary Advisor)

The map shows research collaborations by institution from the past 7 years.
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