Qualifications & Memberships
My recent research has focused on developing algorithms for numerical optimization problems, designing tree-based learners for classification problems and developing spatially balanced sampling designs for environmental monitoring.
- Curran MF., Cox SE., Robinson TJ., Robertson BL., Rogers KJ., Sherman ZA., Adams TA., Strom CF. and Stahl PD. (2019) Spatially balanced sampling and ground-level imagery for vegetation monitoring on reclaimed well pads. Restoration Ecology 27(5): 974-980. http://dx.doi.org/10.1111/rec.12956.
- Kermorvant C., D'Amico F., Bru N., Caill-Milly N. and Robertson B. (2019) Spatially balanced sampling designs for environmental surveys. Environmental Monitoring and Assessment 191 524: 7. http://dx.doi.org/10.1007/s10661-019-7666-y.
- Wickramarachchi DC., Robertson BL., Reale M., Price CJ. and Brown JA. (2019) A reflected feature space for CART. Australian and New Zealand Journal of Statistics 61(3): 380-391. http://dx.doi.org/10.1111/anzs.12275.
- Robertson BL., McDonald T., Price C. and Brown J. (2018) Halton iterative partitioning: spatially balanced sampling via partitioning. Environmental and Ecological Statistics 25(3): 305-323. http://dx.doi.org/10.1007/s10651-018-0406-6.
- van Dam-Bates P., Gansell O. and Robertson BL. (2018) Using balanced acceptance sampling as a master sample for environmental surveys. Methods in Ecology and Evolution 9(7): 1718-1726. http://dx.doi.org/10.1111/2041-210X.13003.