Dr. Pratheep Annamalai is a polymer and nanomaterials scientist with a keen interest in engineering materials for sustainable living. He is an Adjunct Senior Research Fellow at the School of Agriculture and Food Sciences. He has extensive expertise in both translational and fundamental research using nanotechnological tools towards sustainability. Currently, he is interested in alternative proteins and valorisation of agricultural crops and food waste into reactive, building blocks for improving the performance and utility of bioproducts. Thematically, his research focuses on
Food Processing (plant-based food products)
Bioproducts (from agri-food waste)
Sustainable building blocks (for advanced materials).
Before joining UQ, Pratheep studied Chemistry in University of Madras, received PhD in Chemistry from University of Pune (India), then went on to work as a postdoctoral researcher on hydrophobic membranes at the Université Montpellier II (France), and on ‘stimuli-responsive smart materials’ at the Adolphe Merkle Institute - Université de Fribourg (Switzerland).
Upon being instrumental in the discovery of ‘spinifex nanofibre nanotechnology’ and establishing Australia’s first nanocellulose pilot-plant, he has been awarded UQ Excellence awards for leadership and industry partnerships for 2019. Recognising his contribution to the nanomaterials, polymer nanocomposites, polymer degradation and stabilisation regionally and globally, he has been invited to serve as a committee member for ISO/TC229-WG2 for characterisation of nanomaterials (2016), a mentor in TAPPI mentoring program (2018), guest/academic editor for various journals (Fibres, Int. J Polymer Science, PLOS One). He has served as a member of the UQ-LNR ethics committee for reviewing the applications (2017-) and a member of the AIBN-ECR committee in 2014.
I am specialised in remote sensing, Earth observation (EO), and geospatial data science. My current research centres on modelling canopy height by integrating GEDI LiDAR and Sentinel-2 satellite data. This work leverages high-performance computing (HPC), cloud platforms, and machine learning (ML) to produce scalable, reproducible workflows for environmental monitoring and decision support.
My current focus includes:
EO-driven canopy height estimation for forest structure and biomass monitoring
Scalable HPC/cloud workflows for processing LiDAR and satellite data
AI/ML applications in EO for ecosystem modelling and climate resilience
Supporting policy and land planning with EO-derived environmental indicators
Previously, I led research on satellite-based grassland curing to improve fire monitoring, addressing inter-satellite variability and product accuracy for use by fire managers. I also contributed to government-led programs focusing on environmental water planning and provide the framework for coordinating the development of flood works.
I collaborate with academic, government, and industry stakeholders nationally and internationally to ensure EO science translates into tools and insights that support environmental monitoring.
