Data to Intelligence Research Centre

We use advanced data science technologies and theoretical approaches for automatic data analysis, planning and decision-making to solve problems in the domains of health, food, agriculture, defence, energy, environment, finance and education.

Data analytics and machine learning

High-performance data mining

Mining data, recognising patterns, big data dimensionality reduction and visualisation, clustering and classifying data, modelling interactions and causality to better understand systems and behaviours

Computational diagnostics for intelligent health care

Using advanced methods for non-linear and dynamical signal processing and time series analysis for data-driven modelling of human behaviour, processes of sleep, cardio-respiration, diabetes, mental health, age-related conditions.

Modelling complex systems

Developing advanced mathematical and computational models based on dynamical systems and differential equations, with applications to physiology, health, medicine and defence.

Machine learning

Using and developing complex models, algorithms and machine learning-driven knowledge system development and management to make accurate predictions and obtain meaningful insights from data. Investigating and developing approaches to ensure AI systems behaviour remains beneficial to humanity.

Image recognition

Providing efficient algorithms for recognising objects, text, handwriting, images and videos, with advance noise filtering and image reduction techniques in security and intelligent processes.

Computer vision

Aiming to leverage information about the context of acquisition and the image formation process to develop methods, algorithms and techniques for processing visual and multimodal data, requiring the least amount of user intervention possible.

Mathematics

Discrete and continuous optimisation

Providing solutions to large-scale complex problems, such as timetabling, resource allocation, scheduling in the manufacturing industry, facility location planning and supply chain and logistics.

Decision sciences and fuzzy systems

Advanced multicriteria decision-making methods based on cooperative games, non-linear integrals and fuzzy systems.

Graph theory and discrete geometry

Smart networks

Smart city-based traffic network

Developing index techniques and algorithms to manage traffic network data and supporting advanced city planning and scheduling in complex scenarios.

Smart e-business-driven social network

Developing novel social computing models and algorithms for innovating the online advertisement strategies and tracking the evolving changes in social media trends.

Smart biotechnology information network

Designing the AI-powered modern database system to build the bio-information knowledge graph and innovate the lifecycle of health ecosystems.

Data Analytics Lab

The Data Analytics Lab is conducting internationally recognised research to discover data patterns, properties of data and model complex systems. We build data-driven models and intelligent algorithms to solve fundamental and industrial real-world problems.

Mathematics Lab

The Mathematics Lab brings together mathematicians and data scientists within the School of Information Technology, consolidating theoretical and practical research in decision sciences, computational mathematics and optimisation, applied statistics and mathematical modelling.

Knowledge Mining and Meta Intelligence Lab

The Knowledge Mining and Meta Intelligence (KMMI) Lab aims to develop intelligent algorithms that can bridge the knowledge gap between humans and machines through building automated capabilities and bringing together the physical and virtual worlds. The mainstream research of the lab focuses on two themes: Knowledge Mining and Meta Intelligence.

Machine Intelligence Lab

The Machine Intelligence Lab conducts world-class research to harness the transition from a process-defined world to a data-driven one by creating and developing future AI technologies and techniques that will have transformational effects across the economy and society. We aim to design intelligent algorithms that automate problems of data analysis, planning and decision-making. The lab pursues interdisciplinary research within the areas of pattern analysis, machine learning and AI, aimed at discovering the principles underlying the design, development and deployment of artificially intelligent systems.

Artificial Intelligence in Industrial Applications Lab

The artificial intelligence in industrial applications lab aims to bring together researchers in SIT who are working in common domain areas, such as manufacturing, logistics, energy, or related fields, and those who are keen to work in these areas in the future. Our vision is to create a lab that leverages the skills of our lab members to foster collaboration, achieve excellent research outcomes and impact work with the industry.