Dr. Arnick Abdollahi is a multi‑award‑winning researcher and subject coordinator at the University of Technology Sydney (UTS), specialising in Artificial Intelligence, Data Science, Earth and Space Science Informatics, and Environmental Intelligence. He completed his Ph.D. at UTS and was a research fellow at the Bushfire Research Centre of Excellence, Australian National University (ANU), where he led national AI‑driven sensing initiatives to enhance bushfire resilience. His work has advanced bushfire behaviour analysis, promoted responsible AI in environmental monitoring, and influenced strategies for remote sensing, risk assessment, and disaster preparedness.

Arnick leads transdisciplinary research at the intersection of data science, space technology, and climate resilience. His research focuses on the design and deployment of scalable, data‑centric AI systems with space technology that integrate data engineering, geospatial modelling, advanced analytics, and machine learning to support environmental decision‑making under uncertainty. He currently heads a national agricultural initiative developing AI‑enabled grazing decision‑support systems that fuse climate forecasting, satellite Earth observation, and pasture–livestock modelling to support adaptive, data‑driven management by producers.

He is also leading research to develop a national, data‑driven bushfire risk and resilience framework for Australian grazing ecosystems, integrating machine learning, Earth‑ and space‑based technologies, and land management practices to quantify risk, inform mitigation strategies, and support climate‑resilient decision‑making before and after fire events.

In addition, he leads an international research program spanning Australia, Canada, the United Kingdom, and Germany, in partnership with industry and First Nations organisations. This initiative develops AI‑ and satellite‑enabled early‑warning systems for flash drought detection in complex grazing environments, advancing predictive analytics for climate‑resilient land management across diverse climatic systems.

In his teaching role, Arnick coordinates and lectures postgraduate subjects within the Master of Data Science and Innovation (MDSI), a world-leading program of study in analytics and data science at the UTS Transdisciplinary School, including core and advanced subjects in machine learning, artificial intelligence, natural language processing and data science practice. He leads collaborative teaching teams and designs, evaluates and delivers problem‑based, transdisciplinary learning experiences with strong connections to real‑world challenges, industry engagement and applied research. His teaching emphasises responsible AI, hands‑on analytics, and the integration of data science theory with practical decision‑making in complex, real‑world contexts.

Arnick’s academic service spans postgraduate education, scholarly publishing, national research capabilities, and community engagement. He serves on postgraduate assessment panels at the UTS Transdisciplinary School and supervises honours and postgraduate students, as well as research assistants, contributing to capacity‑building and researcher development. He has chaired international webinar and conference series on advances in machine learning, Earth and space technologies, environmental intelligence, and data science. He is an active contributor to the international academic community through editorial leadership and professional service, having served as Main Editor for multiple special issues in field‑related journals and reviewed manuscripts for more than 35 international journals. His service also includes delivering invited talks at workshops, webinars, and conferences, as well as research presentations at national and international forums and conferences.

Arnick has received numerous grants and accolades in recognition of his impact and groundbreaking work, including being named the Rising Star of the Year at the 2025 Australian Space Awards—Australia’s most prestigious recognition for emerging leaders in the Earth and space sector—and being recognised at Australia’s foremost national AI honour, the 2024 and 2025 Australian AI Awards, for AI Academic/Researcher of the Year and AI Rising Star of the Year, respectively. He has also been internationally distinguished as a World's Top 2% Scientist in the Stanford global ranking (2025). 

Current Projects:

• Strategic Research Project: AI-Driven Sensing of Bushfire Risk in Australian Grazing Systems.
• Foragecaster: Build an operational and accurate AI-driven sensing prediction tool for livestock producers to enable optimised farm management and business decision-making in the face of climate variability.
• Worldwide Universities Network (WUN) Research Development Project: Advancing EarthSense AI-driven Livestock Farm Management for Climate-Resilient Food Security.
• Strategic Research Project: AI-Driven Advanced Radar Imaging for Optimised Pasture Feed Quality and Production.

Past Projects:

• The Australian Research Data Common (ARDC) Bushfire Data Challenges program: Designed and implemented scalable data engineering and machine learning workflows to aggregate, harmonise, and analyse multi-source fuel attribute datasets, producing nationally consistent data products for bushfire behaviour modelling, risk assessment, and decision support across Australia.
• Bushfire Risk Sensing Framework: Developing an effective, responsible AI-driven sensing framework to predict fire hazards and limit the increasing occurrence of bushfires in Australia.
• UTS Strategic Research Funding: Forecasting Grass Pollen with Satellite Sensor Time-series, Meteorology Data, and Machine Learning Tools.
• Maxar Spatial Challenge: Monitoring land type features with high-resolution remote sensing and deep learning.
• CSIRO’s national bushfire intelligence capability (NBIC) project: Develop automated machine learning–enabled workflows for generating nationally consistent, spatially continuous, and temporally dynamic layers of vegetation and fuel parameters that are multi-functional in application.