Programme Lead: Dr LI Xiaoli

Data science is essential for many important and/or upcoming technologies, such as artificial intelligence, internet of things, financial technology, robotics, and autonomous vehicles. It has been widely applied with great effect in banking and insurance, telecommunications, transportation, manufacturing and healthcare, etc. The goal of data science is to extract novel and actionable knowledge from different forms of data. It is multidisciplinary, involving data analysis fields such as statistics, data mining, databases, text mining, logical reasoning, and machine learning.

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Active Perception for Object Recognition
Big Code Encoding for Automated Functional Bug Detection
Big data analysis for Smart City with Internet of Things
Big Data in Biomedical and Bioinformatics
Body Dynamics Simulation with FEM
Cellular Imaging and Pattern Recognition
Data Driven Deep Modelling
Deep Learning in Big Data Analytics
Distributed machine learning over heterogeneous infrastructures
Distributed Sensing & Analytics for Smart Manufacturing
Dynamic and Stream Graphs Management
Event extraction from data streams
HCI-Aware Big Graph Analytics for Tom, Dick, and Harry
Heterogeneous Data Analytics for Scholarly Big Data
Interactive Machine Learning and Optimization
Knowledge-Aware Learning with Dynamic Heterogeneous Network Empowered by Social Big Data
Machine Comprehension and Question Answering with Deep Learning Models
Machine Learning for Smart Buildings
Mobility-On-Demand (MoD) for Future Cities
Natural Language Processing with Structured Recurrent Neural Networks
Online Graph Analysis for Massive Network Data
Policy and Data Analytics for Sustainable Cities
Preference Analytics in Large Datasets
 Sample Selection and active learning in Big Data Analytics
 Self-diagnosing, self-correcting, and helpful analytic system
 The Traffic Cop with 24/7 Intelligent Vision
 Traffic estimation and prediction in a smart city
 Urban Computing for Smart Cities
 Vulnerability Patching Leveraging Big Software Data