Scientific computing involves, in 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.
This is a Research Grants Council (RGC)/ National Natural Science Foundation of China (NSFC) joint research grants to support collaborative research between HKUST and Shenzhen Institute of Advanced Technology on blood fluid simulations with HK$1.17M from RGC and RMB 1M from NSFC.
This is an ITF project with the industry partner WeGene. In this study, it aims to develop statistical and machine learning methods for human genomic big data analysis.
In this project, a computer aided anomaly detection system based on state-of-the-art data analytics and machine learning techniques is designed. The goal is to improve the quality and efficiency of the current anomaly detection system in industry.