Darren Yates

Dr Darren Yates

Computer Scientist

Computing, Mathematics and Engineering

Biography

Darren is a post-doctoral research data scientist and full-stack web developer.
He has joined the Gulbali Institute to develop decision support and machine-learning tools for agriculture in a FoodAgility CRC research project, in conjunction with Sunrice and AgriFutures.
Darren created the CLOWD (Combined Location Online Weather Data) web app - free to use - https://clowd.csu.edu.au.
and the  RiversNearMe web app for NSW water-level and flow-rate data - free to use - https://riversnearme.csu.edu.au
He completed a PhD in data science/machine-learning (graduated 8 February 2021). His research topic: new data-mining and exploration techniques on smartphones, including mixed-reality and distributed data mining (received the Faculty Outstanding Thesis Prize for 2021 academic year).

Research
  • Data Mining
  • Algorithms
  • Artificial Intelligence
Publications
Full publications list on CRO

Recent Publications

  • Clarke, A., Yates, D., Blanchard, C., Islam, M. Z., Ford, R., Rehman, S.-U., & Walsh, R. P. (2024). Integrating Climate and Satellite Data for Multi-Temporal Pre-Harvest Prediction of Head Rice Yield in Australia. Remote Sensing, 16(10), 1815. https://www.mdpi.com/2072-4292/16/10/1815
  • Clarke, A., Yates, D., Blanchard, C., Islam, M.Z., Ford, R., Rehman, S. & Walsh, R. (2024): The effect of dataset construction and data pre-processing on the eXtreme Gradient Boosting algorithm applied to head rice yield prediction in Australia, Computers and Electronics in Agriculture,Volume 219, 108716, https://doi.org/10.1016/j.compag.2024.108716
  • Yates, D., & Islam, Z. (2022). Data mining on smartphones: An introduction and survey. ACM Computing Surveys, 55(5), 1-38. Article 101. Advance online publication. https://doi.org/10.1145/3529753
  • Yates, D. (2021). Development and Implementation of Locally-Executed Data Mining on Smartphones. [Doctoral Thesis, Charles Sturt University].
  • Yates, D., & Islam, M. Z. (2021). FastForest: Increasing random forest processing speed while maintaining accuracy. Information Sciences, 557, 130-152. Advance online publication. https://doi.org/10.1016/j.ins.2020.12.067