I'm building the tools, skills and breeding strategies that turn complex crop science into practical progress, wearing several hats along the way:
| 🧬 Quantitative geneticist & plant breeder | Genomic selection, GWAS, mixed models and breeding-programme design, from IRRI's Rice Breeding Innovation Platform to programmes worldwide |
| 📦 R & Shiny developer | Production R packages and Shiny platforms: DataViz Pro, KropAnalytiX, BreedPilot, TSOpt and ShinyAIM |
| 🌐 Website builder | Fast, modern websites, for example plantnura.com and this site |
| ☁️ Cloud deployment on AWS | Containerised scientific apps hosted on AWS, for example DataViz Pro |
| 🚀 Founder & CEO, PlantNura | Education, software and consultancy that bring elite breeding science to every scientist, everywhere |
2014–2017 2018–2019 2019 onwards Today
Ph.D. ───────────▶ Postdoc ──────────────▶ Scientist ──────────▶ Founder & CEO
Plant Breeding Quantitative Genetics Rice Breeding PlantNura
Univ. of Nebraska Animal Science, UNL IRRI
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Every figure your research needs, publication-ready from the first render. A no-code workspace with 150+ plot types in 31 modules, from box plots and regression to Manhattan, LD, circos and population-structure plots, with data cleaning and genotype-file conversion built in. |
Every analysis a breeding programme runs, in one no-code workspace. Takes your field book, marker file or pedigree from upload to a result you can defend, with diagnostics and a report beside every result. |
Every breeding decision, tested before it is made. An AI breeding assistant built on in-house algorithms that reasons like an experienced breeder: which parents to use, which crosses to make and what to select, simulated before a seed is planted. |
| Project | What it is |
|---|---|
| 🎯 TSOpt | R package for training-set design in genomic prediction: decides which genotyped lines to phenotype, exactly and in milliseconds, for any crop and breeding system |
| 🔗 Connected Breeding | Stochastic simulation of managed donor inflow into recurrent genomic selection, the companion code for an integrated framework for salt-tolerant rice (Plant Communications) |
| 📊 ShinyAIM | Shiny app for interactive Manhattan plots in longitudinal GWAS (Plant Direct, 2018) |
| 📘 R for Plant Breeding | Open learning resource for plant breeding and genetics data analysis and visualisation in R |
| 🔁 Analysis pipeline | Open-source pipeline for robust breeding-trial analysis, visualisation and sharing (Plant Methods, 2022) |
| 🎓 Fundamentals of Genomic Prediction | Course material for the genomic prediction course I taught at IRRI |
| 🧮 Mixed Models | Learn mixed models step by step and extract BLUEs and BLUPs |
- Hussain W, Anumalla M, Zhang X, Ismail AM (2026). Translating flood-tolerance biology into breeding: a five-dimensional framework for next-generation rice varieties. Journal of Experimental Botany. doi
- Anumalla M, … Hussain W (2025). Salinity-tolerant rice: a sustainable solution for food security and greenhouse gas mitigation. Current Plant Biology. doi
- Hussain W, Anumalla M, Ismail AM, et al. (2024). Revisiting FR13A for submergence tolerance: beyond the SUB1A gene. Journal of Experimental Botany. doi
Focus areas: genomic selection · GWAS · mixed models · breeding-programme optimisation · genetic gain · stress-tolerant rice
🤝 Open to collaborations in breeding analytics, training and strategy. Let's talk.