Human centred computing
As technology becomes increasingly ubiquitous, our relationship with it is changing.
Our facilities
We boast an incredible array of facilities, making our innovative human centred computing research possible.
We are treating technology less as a tool, and more as a collaborator. In the move towards more sophisticated automation it is increasingly important that everything we design - from cars to personal assistants to algorithms - has human understanding, behaviour and ethics at its centre.
In the School of Computer Science, human centred computing researchers are working in diverse fields including healthcare, robotics, virtual reality, media and beyond, to pioneer new forms of technology that will transform our lives, in exactly the ways we want them to.
Areas of expertise
Our researchers focus their work in the following specialist areas:
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Psychological aspects of UX design
By understanding human perception, we can build more effective software and hardware. We use eye tracking, analysis of facial expressions and gesture, and interaction analysis (extracting behaviour from mouse movements and key strokes) alongside qualitative methods such as usability evaluation to understand the user experience.
Contact: Caroline Jay
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UX design
Better interfaces mean happier, more productive users. Our UX design work leads the world in accessibility for visually impaired users.
Contact: Simon Harper
Postgraduate research projects
Human centred computing projects
- Automatic Activity Analysis, Detection and Recognition
- Automatic Emotion Detection, Analysis and Recognition.
- Cognitive Robotics and Human Robot Interaction
- Digital Multimedia and Human Memory
- Evaluating Systems for the Augmentation of Human Cognition
- Interactive Sequential Pattern Mining of Web Logs
- Machine Learning and Cognitive Modelling Applied to Video Games
- Pervasive Technology for Multimodal Human Memory Augmentation
- Smart Security for Smart Services in an IoT Context
Ke Chen projects
- Automatic Activity Analysis, Detection and Recognition
- Automatic Emotion Detection, Analysis and Recognition.
- Biologically Inspired Deep Learning for Speech Information Processing
- Contextualised Multimedia Information Retrieval via Representation Learning
- Deep Learning for Temporal Information Processing
- Ensemble Strategies for Semi-Supervised, Unsupervised and Transfer Learning
- Information Component Analysis via Deep Learning
- Information Component Analysis via Deep Learning
- Machine Learning and Cognitive Modelling Applied to Video Games
- Multi-task Learning and Applications
- Zero-Shot Learning and Applications