Supervision
Research guidance
Research questions, experimental design, crop improvement, quantitative analysis and scientific interpretation.
Teaching
From research question to defensible conclusion.
I teach quantitative and biological subjects with one recurring logic: research question → design → data structure → analysis → assumptions → output → biological interpretation → scientific conclusion.
CSP 509Biostatistics & Field ExperimentationCalculator · Excel · R · VivaSense
CSP 811Biometrical GeneticsQuantitative genetics
Subject areaGenetics & Plant BreedingFoundations · application
Subject areaGIS & Spatial ThinkingApplied
Teaching philosophy
Software is not statistical reasoning.
I want students to understand what an analysis represents, why it is appropriate, how the data should be organised, what assumptions matter and what conclusions the evidence can actually support.
In quantitative courses, practical work can move deliberately from calculator → Excel → R → VivaSense: first understanding the calculation, then structuring the data, then analysing reproducibly, then checking and interpreting the result in a design-aware environment.
Documented teaching portfolio
A broader crop-science teaching record.
University records show teaching across undergraduate and postgraduate courses including CSP 201, CSP 204, CSP 306, CSP 316, CSP 323, CSP 409, CSP 417, CSP 502, CSP 511, CSP 708, CSP 807 and CSP 809, alongside BTH 201 and BTH 204.
Supervision record →
Open learning
Field-to-Insight Academy
Open and structured learning resources that extend practical agricultural research education beyond one university class.
Analysis environment
VivaSense
A design-aware agricultural analysis platform used alongside R and other tools for learning, checking and scientific interpretation.