Welcoming Our New RIDR Group Members!
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We are excited to introduce three new researchers joining the RIDR group this year: Isabelle Tingzon, Ayush Prasad, and Fan Wang. Their diverse expertise in remote sensing, AI, and physics-guided ML will strengthen our work on tackling environmental problems with AI and machine learning.
Isabelle Tingzon – AI for climate resilience & social good
Isabelle joins us with experience on applying AI and geospatial data science to pressing global challenges. She was previously a geospatial data scientist at the World Bank, leading initiatives on AI-driven climate resilience—from mapping building stock characteristics with drones and satellite imagery to supporting climate adaptation strategies in vulnerable regions.
Her previous work includes developing AI-enabled school mapping at UNICEF’s Giga initiative, generating nationwide poverty maps in the Philippines, and accelerating humanitarian response during the Venezuelan migration crisis.
Isabelle has received awards such as the Women in AI Asia-Pacific “AI for Social Good” Award (2024) and has presented her work at major conferences including AAAI, ICCV, and Climate Informatics.
She is also a core member of Climate Change AI, where she leads tutorials to advance the responsible use of AI for climate action.
Ayush Prasad – machine learning for remote sensing
Ayush recently completed his MSc in Theoretical and Computational Methods at the University of Helsinki. His work lies at the frontier of physics-guided machine learning and remote sensing, with applications in climate science.
Previously at the Finnish Meteorological Institute, he developed models for snow-on-sea-ice prediction, integrating them into Earth system models. He has also been a research intern at MILA – Quebec AI Institute, building self-supervised generative models for climate downscaling, presented at the ICML workshop on Machine Learning for Earth System Modeling 2024.
Ayush’s research spans from marine heatwave forecasting to ecosystem modeling pipelines (PEcAn, Google Summer of Code), and he has co-authored several recent papers in Environmental Data Science and ICML workshops.
Fan Wang – remote sensing & environmental monitoring
Fan joins us from Lund University, where she completed her MSc in Geomatics. Her research focuses on machine learning for environmental monitoring, vegetation mapping, and model–data fusion.
She has experience working with startups as an R&D Engineer, developing remote sensing services for agriculture, and is currently a research assistant in the TreeSpec project at the Centre for Environmental and Climate Science, Lund University, where she is helping to deliver large-scale tree species maps using Earth observation and machine learning.
Fan brings expertise in geospatial machine learning, and is making her research accessible through open-source tools.
Looking Ahead
We are thrilled to welcome Isabelle, Ayush, and Fan to the RIDR family. Their expertise in remote sensing, AI for social good, and physics-informed machine learning will strengthen our research on resilience, climate adaptation, and sustainable futures.
Stay tuned for updates as they dive into new projects and collaborations within the RIDR group!
