The Agricultural Scientist and Engineer

I am an agricultural scientist and engineer with 19 years’ experience across research, industry and engineering roles in Australia, France and Iran, combining technical expertise in crop physiology, hydrology, agrometeorology, cropping systems modelling and data science with strong, formally trained project management capability. In addition to my early career as a senior engineer and project manager in Iran, I have held major research roles at the University of Queensland, James Cook University, and the University of Southern Queensland, as well as earlier positions with INRA-SupAgro and Limagrain Europe in France. Across these roles, I have led complex, multi-institutional projects that required structured planning, risk assessment, stakeholder coordination, timeline and budget control, and delivery of measurable outcomes. I continue to strengthen this capability through recognised project management training aligned with PMP standards.

My research brings together crop, soil, and ecosystem processes to develop strategies that improve productivity, profitability, and sustainability. Through experimentation and simulation, I investigate the mechanisms driving productivity, resource efficiency, and climate resilience across diverse farming and hydrological systems. I design and evaluate management options that enhance water- and nutrient-use efficiency, quantify the effects of abiotic stress and G×E×M interactions, and assess adaptation pathways under climate variability and change. By combining empirical field data, remote sensing, and process-based modelling, I translate scientific understanding into practical tools and frameworks that guide decision-making. 

My early career as an engineer/senior engineer and project manager in water engineering projects grounded my scientific work in practical systems thinking and real-world implementation. Leading feasibility studies, modelling, hydraulic design, and construction oversight for more than 20 projects, I applied quantitative modelling, optimisation, and geospatial analysis to improve irrigation efficiency, drainage performance, and on-farm and catchment water management. This experience continues to inform my research ethos—where engineering design, computational modelling, and data-driven analysis intersect to deliver applied, outcome-oriented innovation.

Across my career, I have secured AUD 2.5M (1.4M in Australia) in competitive grants as lead/co-lead, chief investigator, or supervisor and contributed to the delivery of over AUD 4.6M (3.5M in Australia) in research projects. I have led, co-led, or supervised independent and multi-organisational, cross-disciplinary grant proposals and Expressions of Interest seeking more than AUD 18.9M (17.9M in Australia) in funding, involving over 20 partner organisations across national and international consortia. My research record includes 89 peer-reviewed papers, 3 books, 66 conference presentations, ~1,900 citations, and an H-index of 20 (November 2025). I have produced numerous technical reports and supervised or co-supervised 15 HDR and MSCN students.

The Data Scientist

Since the beginning of my undergraduate studies, data science (see “Technical Skills”) has been a central pillar of my work as both an engineer and a researcher. I have continually strengthened this capability through sustained self-training across a wide range of tools, software applications, frameworks, and contemporary data-science practices. I design and deploy data-driven solutions that integrate simulation modelling, remote sensing, statistical analysis, machine learning, optimisation algorithms, and geospatial intelligence to improve efficiency, resilience, and decision-making. My extensive training enables me to translate this expertise across sectors, supporting high-quality business-intelligence and data-analysis solutions in fields such as finance, retail, and customer service.

I have developed advanced computational frameworks that combine large-scale ETL workflows, distributed analytics, and cloud-ready architectures. My daily work draws on R (e.g., data.table, tidyverse, tidymodels, ggplot2, Shiny), Python (e.g., Pandas, NumPy, SciPy, scikit-learn), and a range of SQL environments (MS SQL Server, MySQL, PostgreSQL, SQLite) to manage datasets exceeding billions of records across formats such as SQL tables, CSV/TSV, JSON, Parquet, GeoTIFF, shapefiles, and scientific arrays (e.g., NetCDF, HDF5). I routinely develop predictive and optimisation models using scikit-learn, caret, Azure ML, Databricks ML, MLflow, Spark MLlib, XGBoost, randomForest/ranger, and LightGBM, supported by extensive experience and training in HPC, cloud engineering, and data warehousing through Azure (e.g., Data Factory), AWS (e.g., EC2, S3), Databricks (e.g., Unity Catalogue, Delta Lake), MS Access, and SQL. I also create automated, reproducible pipelines for calibration, validation, uncertainty analysis, ETL, exploratory data analysis, visualisation, and reporting using Excel, Power BI, Tableau, and R/Python. These capabilities underpin a mature, end-to-end analytics workflow that delivers scalable, offline and cloud-ready decision-support solutions for climate resilience, resource-use efficiency, and agricultural innovation.