About Me
Research Fellow in Curtin Biometry & Agricultural Data Analytics
Dr. Zhanglong Cao
Research Fellow in Curtin Biometry & Agricultural Data Analytics
Curtin University, Perth, Australia
Professional Overview
I am a statistician and biometrician specializing in advanced statistical methods for agricultural applications. Currently working as a Research Fellow in the Curtin Biometry and Agricultural Data Analytics (CBADA) team, I focus on developing and applying cutting-edge statistical methodologies to solve real-world challenges in agricultural science.
Research Expertise
Linear Mixed Models (LMM)
Advanced modeling for complex agricultural data
Bayesian Statistics
Probabilistic inference and uncertainty quantification
Markov Chain Monte Carlo (MCMC)
Computational methods for complex models
Experimental Design
Optimal design strategies for agricultural trials
Geostatistics
Spatial analysis and modeling
Smoothing Splines
Non-parametric regression techniques
Applied Statistics in Agriculture
Real-world applications in crop science
On-Farm Experiments
Field trial design and analysis
Current Role & Impact
50+ GRDC-funded Projects
>$43 Million Total Value
High-Impact Research Outputs:
- Peer-reviewed publications in top-tier journals
- R packages and decision-support tools
- Scalable frameworks embedded in national agricultural programs
- Interactive Shiny applications for data visualization
Education
Key Publications
Optimal design for on-farm strip trials
Field Crops Research (2024)
Economic analysis of crop protection strategies
Pest Management Science (2024)
Bayesian inference for spatially correlated parameters
Field Crops Research (2022)
V-Spline: Adaptive smoothing for trajectory reconstruction
Sensors (2021)
Interactive Tools & Applications
Yield Response Curves (YRC) Project
Comprehensive analysis of wheat and barley variety responses
🌐 Live ApplicationCrop Protection Analytics System
Economic impact analysis for disease management
GRDC Disease and Pest Impact Overview
Visualization tools for agricultural stakeholders
Programming & Technical Skills
R
Statistical computing and data analysis
Python
Data science and machine learning
Julia
High-performance scientific computing
Shiny
Interactive web applications
Git/GitHub
Version control and collaboration
Professional Activities
Conference Presentations
Regular speaker at Australian Applied Statistics Conference
Academic Supervision
Mentoring HDR students in statistical methodology
Industry Collaboration
Working with agricultural researchers and stakeholders
Open Source Development
Contributing to the statistical community
Get in Touch
"Statistics is the science of learning from data, and I'm passionate about making this learning accessible and impactful for real-world applications."