Gaurangi Anand

Commonwealth Scientific and Industrial Research Organisation, Queensland University of Technology

Papers

Total Citations

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About

Gaurangi Anand is a researcher at the intersection of artificial intelligence and environmental science, with key contributions in ecotoxicology and time-series analysis. Her most cited work, "Graph neural networks-enhanced relation prediction for ecotoxicology (GRAPE)" (2024, 12 citations), pioneers the use of Graph Neural Networks (GNNs) to integrate aquatic toxicity data, offering a computational alternative to traditional in vivo ecotoxicity testing. This novel approach addresses the urgent need to predict chemical impacts on species and ecosystems, reducing reliance on animal testing. Her earlier work, "DeLTa: Deep local pattern representation for time-series clustering and classification using visual perception" (2020, 11 citations), introduces an innovative method for time-series analysis by leveraging visual perception principles, advancing clustering and classification tasks. Together, these contributions demonstrate her ability to bridge deep learning with practical environmental challenges, earning her recognition for applying cutting-edge AI to toxicology. With a growing citation impact, Anand’s research is shaping the future of computational ecotoxicology and data-driven environmental protection.