TSNet
: Python Package for Transient flow simulations in water networks 
Research
Summary
My research interest is to leverage numerical modeling, algorithm development, remote sensing, and artificial intelligence to
- develop physics-based and data-driven models to achieve high-performance modeling of water infrastructure systems and enhance the coordination between interdependent urban infrastructure systems;
- improve the operation, control, and management of water infrastructure systems; and,
- promote the integration of technical and social components in the development of smart cities.
Projects
Transient Event Detection and Pattern Discovry
Time serires data mining approach to detect and discover recurrent pressure transient patterns without prior knowledge 
Model-based Sensor Placement under Uncertainties
This project studies the optimal sensor placement problem for event identification in dynamical systems considering the balances between model accuracy, parsimony and uncertainties 
Graph Neural Networks for State Estimation
Learning-based approach for state estimation in water distribution systems 