University of Queensland (UQ)

From November 2017 to February 2021, I worked as a Research Fellow in Crop Modelling and Physiology at the University of Queensland, where I led and contributed to multidisciplinary research across crop physiology, climate adaptation, and digital agriculture. My work focused on understanding how drought, heat, and other abiotic stresses influence wheat performance in Australia’s variable environments, using advanced process-based models such as APSIM and APSIM NextGen. I designed and participated in extensive field and glasshouse experiments that evaluated 105 globally sourced wheat genotypes for stress tolerance, including the use of an automated lysimetric platform. These experiments generated the physiological insights required to identify promising adaptive traits and inform breeding strategies for future climates. I also implemented a new photosynthesis module in APSIM NextGen to evaluate limited-transpiration traits, and collaborated with UQ, CSIRO, and ANU colleagues to integrate and validate the DCaPST canopy photosynthesis–stomatal conductance module. Alongside this, I carried out large-scale in silico investigations on HPC clusters and used drone-based multispectral imaging to assess trait expression across environmental gradients.

In parallel, I built a strong data-science capability to support simulation modelling, ETL workflows, and advanced analytics across twelve research projects. I developed scalable R and Python pipelines to manage extremely large datasets, including 5–10 billion-record simulation outputs and a 40-million-record wheat-trait database used to quantify heat-shock impacts across the Australian Wheatbelt. My work included explanatory data analysis and machine learning using R (ggplot, data.table, tidyverse) and Python (Pandas, NumPy, scikit-learn, matplotlib, seaborn), as well as SQL-based data warehousing and dashboarding with Power BI and SSRS. I managed national to global datasets in SQL, NetCDF, RData, CSV, XML, Excel, and JSON, and evaluated long-term drought and climate trends using CRU gridded datasets (1957–2016). I also published research demonstrating the plausibility of training neural networks (Python/Keras) to emulate APSIM-based predictions of wheat phenology and yield, showing the potential of hybrid modelling approaches that combine mechanistic and machine-learning methods.

My role at UQ also involved significant leadership, stakeholder engagement, and research supervision. I provided technical and scientific leadership across multiple crop-modelling and digital-agriculture projects, coordinated co-design activities with UQ, CSIRO, ANU, and industry partners, and contributed to the development of decision-support tools and modelling workflows tailored to end-user needs. I supervised Honours and HDR students, delivered presentations at national and international conferences, and worked closely with industry, breeding programs, and government partners to translate complex data and modelling insights into practical, actionable outcomes. Through these combined activities, I helped advance UQ’s capability in crop modelling, trait evaluation, and climate-resilience research, contributing to high-impact publications and strengthening Australia’s modelling capacity for future agricultural challenges.

Projects