To enable the emerging machine learning technologies and IoT intelligence in fast and robust emergency response, we aim to develop novel algorithms for flooding monitoring, human behavior analysis, decision making, and UAV controling in Water Resource Engineering. The innovations focus on advanced methods of computer vision, reinforcement learning, large foundation models and game theory.

To leverage the physics models in machine learning to address the limited quantity and unsatisfying quality of data in realistic applications, we aim to develop physics-inspired machine learning by integrating physical principles and remote sensing data, with broad applications in carbon, phytoplankton, and nutrions in coastal waters. 

To characterize the distribution of biomedical data to ensure the efficient data storage, real-time disease diagnosis, precise time-series signal processing, and fast data transmission, we aim to investigate the application of self-supervised learning and generative models in smart health, covering applications like data compression, modality transformation, signal reconstruction and biomedical data security.