Abhilash Singh
University of Leeds, Indian Institute of Science Education and Research Bhopal, Indian Institute of Science Education and Research Tirupati
0000-0001-6270-9355
About
I work at the intersection of artificial intelligence, Earth observation, hydrology, and weather/climate science. My research develops physics-informed and data-driven machine learning models to transform satellite, radar, sensor, and environmental datasets into actionable information for soil moisture monitoring, hydrological prediction, precipitation nowcasting, drainage congestion assessment, and climate-risk analytics. At the University of Leeds, I am a Research Fellow in Physics-Informed Machine Learning for African Storms and lead the AINPP Africa intercomparison initiative, coordinating AI-based nowcasting evaluation with commercial partners, academic institutions, and meteorological agencies. My long-term goal is to build trustworthy and scalable AI systems for Earth and climate intelligence.
Employment
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University of Leeds Research Fellow2024 - Present
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Indian Institute of Science Education and Research Bhopal Researcher2023 - 2024
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University of Leeds
Education
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Indian Institute of Science Education and Research Tirupati Ph.D.2018 - 2024
Projects & Funding
Projects & funding information is unavailable.
Publications (56)
- Transferable Neural Operators to Estimate Subsurface Soil Moisture Save
- Downscaling SMAP soil moisture using a hybrid machine-learning algorithm Save
- Weak Physics‐Guided Multi‐Agent Learning for Surface to Subsurface Moisture Estimation Across Diverse Climate and Soil Conditions Save
- How Important Are the Critical Points in Selecting the Optimal Samples for Accurate Estimation of Subsurface Soil Moisture? Save
- Overcoming data scarcity: A transfer learning framework with fine-tuned neural networks and multi-sensor satellite image fusion for soil moisture estimation Save
- Physics‐Aware Probabilistic Modeling of Subsurface Soil Moisture Using Diffusion Processes Across Different Climate Settings Save
- AutoML-Fire: Automated machine-learning approach to predict forest fires Save
- Leveraging Neural Operator and Sliding Window Technique for Enhanced Subsurface Soil Moisture Imputation Under Diverse Precipitation Scenarios Save
- Assessment of machine learning models to predict daily streamflow in a semiarid river catchment Save
- PIML-SM: Physics-Informed Machine Learning to Estimate Surface Soil Moisture From Multisensor Satellite Images by Leveraging Swarm Intelligence Save