Organization profile

Profile Information

The Machine Intelligence group conducts cutting-edge research in the areas of artificial intelligence, web science, machine learning, computational biology, digital computer games and computer networks.

Much of the group’s research provides intelligent techniques to design systems that: 1) support decision making of human/machine operators interacting with others under uncertainty; 2) analyze large scale of data to provide online prediction and personal recommendations; 3) mine text for knowledge through information extraction and other natural language processing; 4) develop mathematical models to predict and interpret biological and biomedical functionalities and 5) apply machine learning to wireless network and security problems. The group focuses on real-world applications in computer games, robot navigation, social media, biomedical informatics and networks. Recently the group extends its research antenna into big data research and application and the focus is on the use of artificial intelligence based technologies to support actional data mining, data visualization, user modeling and so on.

Fingerprint The fingerprint is based on mining the text of the scientific documents related to the associated persons. Based on that an index of weighted terms is created, which defines the key subjects of research unit

Metabolism Chemical Compounds
Game theory Engineering & Materials Science
Genes Engineering & Materials Science
Genome Medicine & Life Sciences
Systems Biology Medicine & Life Sciences
Learning systems Engineering & Materials Science
Gene expression Engineering & Materials Science
Metabolic Networks and Pathways Medicine & Life Sciences

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Projects 2018 2021

Research Output 2008 2019

A Learning Design Framework to Support Children with Learning Disabilities Incorporating Gamification Techniques

Shaban, A. & Pearson, E., 9 May 2019, CHI 2019: Weaving The Threads of CHI.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearch

Open Access
learning disability

Combining metabolic modelling with machine learning accurately predicts yeast growth rate

Culley, C., Vijayakumar, S., Zampieri, G. & Angione, C., Jun 2019, (Accepted/In press).

Research output: Contribution to conferencePaperResearchpeer-review

Open Access
Metabolic engineering
Gene expression
Industrial applications
Learning systems

Evaluation of a Prototype Interactive Working Memory Application for Children with Learning Disabilities

Pearson, E. & Shaban, A., 12 May 2019.

Research output: Contribution to conferencePaperResearchpeer-review

learning disability
online survey

Press / Media

University awarded prestigious grant to investigate the AI race

The Anh Han


1 media contribution

Press/Media: Press / Media