Modeling to connect big data and decision making

Numerical Simulation · Big Data · Artificial Intelligence · Uncertainty Quantification · Optimization · Crowd sourcing · Sensor Network

Research Vision

Thanks to Artificial Intelligence, we can extract a huge volume of data from the society and natural environment to an extraordinary level of details, velocity, and variety. However, it is still difficult to use the data to support decision-making. We believe the key is numerical modeling, which can effectively integrate data, transform data into information, and make predictions to guide decision making. Our research group aims to address two critical challenges to achieve the goal.

1) Data-Model Interface: how to enrich the big data source and develop a transforming interface to improve model reliability and accuracy.

2) Model-based Decision Making: how to inform decision making taking the advantage of the fast speed and high resolution of the model with large scale supercomputing.

Our lab has a wide spectrum of research topics, including coastal resilience, urban floods, wind energy, aquaculture, multiphase flows, sediment transport, nano-microfluidics, etc.

Our Work

Can a city combat Sea-level Rise alone? A study reveals higher sea-level rise requires wider range of social collaboration.

Which CFD code is better to simulate annular reactors, OpenFOAM or Ansys Fluent? A comparative study.

Big data has a potential to improve the preparation of urban drainage area planning.

Modeling to create the next-generation low-energy drip irrigation.

Twitter + Citizen Science + AI = improved flood data collection

A new data-driven analysis method reveals hidden patterns: the different coastal hydrodynamics responses to tides, sea-level rises, and storm surges.

More past research can be found at the publication section.