Scientific computing involves, in a broad sense, the development of reliable, accurate and efficient computational algorithms that make challenging problems tractable on modern computing platforms, providing scientists and engineers with new windows into the world to solve problems arising from mathematics, engineering, biology, physics and other natural sciences.
Data science stresses the development of tools designed to find trends within datasets that help scientists who are challenged with massive amounts of data to assess key relations within those datasets. It focuses on statistical analysis and machine learning, which are mainly used to extract meaningful information out of data. It is widely applied nowadays in such areas as health care, finance and manufacturing.
This is a Hong Kong Research Grants Council General Research Fund. In this project, a research team led by Professor Yang Xiang has systematically developed continuum models for the energy and dynamics of grain boundaries incorporating the underlying microstructure of line defects (dislocations or disconnections).
In this research project, a research team led by Professor Jianfeng Cai investigated landscape of non-convex optimization arising from phase retrieval, which is a fundamental problem in many imaging techniques such as X-ray crystallography, transmission electron microscopy, and coherent diffractive imaging.
There are four topics in the research area of decentralized finance (Defi). Innovative ideas were extensively reviewed, studied and built into four separate protocols.