Reasearch Interests 

• Hydrological and water resources modelling, forecasting, and simulation
• Data-driven modelling (artificial intelligence, machine learning, deep learning, etc.)
• Data mining
• Time series analysis (e.g., wavelet transforms)
• Big Data applications in hydrology and water resources
• Uncertainty quantification and risk assessment
• Ensemble modelling/forecasting
• Data assimilation
• Water supply systems
• Smart water networks
• Optimization

Current Projects

• Dynamic deep learning for simultaneously forecasting hydrological variables across multiple timescales
• Coupling process-based and data-driven models for improved hydrological prediction
• Application of wavelet transforms to hydrology and water resources with a focus on best practices
• An expert system for optimizing smart water network operations