RESEARCH CENTERS

Introduction of our research centers

HKUST BDI is a multiple-center based institute. Each center has its own strategic area with external support from industry as well as the government. Our goal is to deliver significant international impact and become the leader in the Asia Pacific region in Big Data research. BDI focuses on developing big data technologies in smart city, business intelligence, health and well-being, bio and genetics, e-commerce, privacy, policy, and robotics design.

WHAT LAB


WHAT LAB, which is short for WeChat-HKUST Joint Lab on Artificial Intelligence Technology, is dedicated to foster artificial intelligence and big data research to improve people’s living and advance the frontiers of knowledge, marking a milestone in the collaboration of WeChat and the higher education sector.

WeChat and HKUST will jointly conduct Artificial Intelligence (AI) Technology related research and explore the far-reaching frontiers of AI. Today AI technology is experiencing a tremendous growth, and much of this advance depends on talents, problems and data. WeChat and HKUST complement each other in these aspects, and this collaboration on AI research is expected to be long-term and world-leading. Research areas of WHAT LAB include intelligent robotic systems, natural language processing, data mining, speech recognition and understanding. The Lab will bring together top researchers in the development of innovative artificial intelligence application with the database of WeChat.

HKUST The Big Data for Bio Intelligence Laboratory (BDBI)


The Big Data for Bio Intelligence Laboratory (BDBI) aims to become a leading laboratory in the research of big data for biological intelligence and to bridge the knowledge gap between academics and practitioners. Research areas of the Laboratory include new big data solutions such as “deep learning solution”, which generates rich features to describe a machine learning problem in order to let computers make decisions, and “transductive transfer learning” – allowing computer models to be easily adapted for use in many different application domains. It will also focus on genetic farming with objectives of making the process more automatic and user-friendly and scaling it to cater to very large data sources.

Big Data Platform for Smart Transportation Applications with Heterogeneous Data Sources


HKUST and Thales are carrying out research and development to build a Big Data platform to address two critical problems in the public transport. Indeed, the platform aims at effectively monitoring and directing the crowd in railway stations so that early warnings can be given on potential dangers. It also aims at ensuring smooth operation of railway transport system by predicting potential major equipment failure. The big data platform is, therefore, designed and developed to integrate heterogeneous (ranging from surveillance videos to twitter chats) data and transform them into structure and easy-to- query formats. In addition, a suite of analytic tools is applied to elicit useful patterns and information from the data. Finally, specific application-drive models are applied to automatically monitor the current situations and make forecasts. A closed-loop optimization module is also designed for decision support. 
The platform fuses together many state-of- the-art big-data research topics, such as data fusion, data analytics, human factors, optimization/visualization, transfer learning, simulation and operations research. The inputs are heterogeneous data sources. The outputs are structured data formats that can be queried. The project is unique to the extent that academia, industry and government work closely together to build an interdisciplinary and cross-domain solution for problems pertinent to Hong Kong and other large cities. The Big Data platform helps public transport agencies to build smart transport solutions in moving people efficiently and safely and, hence, enhancing citizen’s quality of living in a smart city.

People-Aware Smart City: A People-Centered Integration, Mining and Decision Framework


In this project, we plan to build a people-aware smart city framework, which focuses on finding people’s needs and satisfy them. This is the first attempt to build such a smart city framework that centers on people, the residents of a city. We proposed a people-aware smart city framework that integrates data extracted continuously from the people, discovers their needs from integrated multi-source data, and finally determines the best resource allocation plans to satisfy these needs. People’s needs from the areas of education, health, travel, safety, finance and entertainment, which all have measurable objectives, will be studied in this project. 
In order to achieve the goals in the framework, several state of art techniques will be developed including data integration solutions to handle different data sources with different formats, transfer learning-based mechanisms to reveal knowledge, and machine-human collaborative approaches to make wise decision. In addition to making breakthrough in technical development of people-aware smart city framework, we will closely work together with our sponsor and partner, China Digital City Forum Limited, to implement our framework into their smart city solutions and demonstrate the effectiveness of our proposal. Moreover, we will try to incorporate the framework into Smart Transportation and Back Alley projects carried out by Energizing Kowloon East Office, to bring the benefits to Hong Kong people.