MIH Media Lab research areas.

The following broad research areas have been identified as priorities to the research team:


  • Semantic Web technologies, marrying the ways in which humans and computers understand the content of the Web, and the purposes of interacting with it.
  • Augmented Reality, a field of computer research which deals with the combination of real-world and computer generated data (virtual reality), where computer graphics objects are blended into real footage in real time.
  • Media Distribution, moving beyond the 20th-century broadcast model of media dissemination towards view-on-demand and peer to-peer television technologies.
  • Social networks,
  • WiMax, ad hoc and mesh networks, progressing beyond a static picture of what networks look like and how data within such networks should be routed (including in this concept not just computer networks, but also networks of autonomous software entities).
  • Human-Computer interaction, a discipline concerned with the design, evaluation and implementation of interactive computing systems for human use and with the study of major phenomena surrounding them.
  • Gaming and collaborative environments, such as the creation and representation of persistent virtual worlds, the creation of artificially intelligent agents within such worlds, the back-end technologies required to efficiently manage such environments, and the network protocols to enhance interaction with these virtual spaces. An important application of this research is MMOGs (massively multiplayer online games).
  • Electronic visualisation, being the representation of complex data in a visual manner that maximises the rate of information flow from machine to human. This involves computer graphics and applied mathematics, particularly the application of graphical models.
  • Human language technologies, including natural language processing, the processing of massive audio datasets, concept extraction, document clustering, meta-tagging and automated human language translation.
  • Search and massive data management, such as the use of sharding to spread data over a vast array of servers, or the time-efficient processing of massive bodies of data. The focus here lies on the recognition of information patterns in data sets, and the grouping of data in information spaces based on recognised features. This also includes providing an interface to a group of items that enables users to specify criteria about an item of interest and finding the matching items.
  • Mobile and Ubiquitous computing,
  • Dynamic profiling, by means of which possibly disparate data on individuals can be aggregated and interpreted in such a way that future behaviour may be predicted probabilistically. The application of this research  can allow software and web technologies to better anticipate users’ needs, and support computing activities pro-actively.
  • Grid computing, including parallel programming and massive Web back-end technologies.
  • Conditional Access where digital content and software needs to be protected from unauthorised distribution and/or access.