School of Informatics

Strands of AI research

Researchers in the School of Informatics explore a number of different strands of artificial intelligence research.


Pascal Voc
Identifying and localising objects (dog, human) in an image.

One strand of AI research concerns perception, where we wish to understand signals in the world such as images, video and speech. For speech, the aim is to turn the acoustic signal into words (research carried out by Centre for Speech Technology), while for vision we may wish to identify objects, agents and their actions (Bob Fisher, VICO, Laura Sevilla, Machine Intelligence Group). We can also combine modalities, e.g. with the joint analysis of speech and video data.

Centre for Speech Technology Research

Bob Fisher

VICO research group 

Laura Sevilla 

Machine Learning Group


As well as perceiving the world, we may want to reason and answer questions about it. Logic provides a powerful framework for this task, as applied e.g. to mathematical theorem proving (Mathematical Reasoning Group).

Another important aspect is being able to reason properly under uncertainty when there is incomplete information, for example in a medical application where there may be multiple possible diagnoses, some more likely than others (Machine Learning Group).

Mathematical Reasoning Group

Two class figure IAML
A simple two-dimensional supervised learning problem

Machine Learning Group

Machine Learning

Artificial intelligence systems need to adapt and learn (Machine Learning Group). In supervised machine learning, labelled data is provided, along with an evaluation of how well the task is being achieved, and the system adapts to improve its performance. For example, a system can be trained to label the objects in an image, like a bird or a car. Such problems have benefited from recent developments in deep learning. We also develop algorithms for unsupervised learning, to detect such structure in the data without the benefit of labels.

Machine Learning Group


Robotic arm
Measuring human muscle activations to reproduce movements with a robot arm

We build agents that can act in their environment to achieve useful tasks, be it the physical world for robots, or virtual worlds for softbots. Such agents need to learn, reason and plan, whether they are acting fully autonomously, or in collaboration with humans and computer-based entities. Controlling dextrous motion and physical interaction (Michael Mistry, Zhibin Li) provides the basis for intelligent action and control of robots (Statistical Machine Learning and Motor Control Group, Steve Tonneau). Generating such actions can also be applied to virtual agents in computer graphics and animation (Computer Graphics and Visualization Research Group). Concepts of autonomous decision making can focus on the coordination and cooperation in multi-agent systems (Autonomous Agents Research Group), and be extended to other domains such as predictive modelling and decision making in energy and environmental systems (Robust Autonomy and Decisions Group).

Michael Mistry

Zhibin Li

Statistical Machine Learning and Motor Control Group 

Steve Tonneau

Computer Graphics and Visualization Research Group

Autonomous Agents Research Group

Robust Autonomy and Decisions Group


Machine translation demo
Example output of machine translation from English to Sesotho (an African language)

Language is a critical route by which we communicate and convey information to each other. We work on algorithms that understand the structure of sentences (The Cohort research group), determine the meaning of the linguistic input, and then generating appropriate responses (Natural Language Processing Group). Natural language understanding and generation can be deployed in range of areas; particular strengths in Edinburgh include machine translation (Statistical Machine Translation Group), document summarization (Extreme Summarization), and information retrieval, including from social media (Walid Magdy).

The Cohort research group

Natural Language Processing Group

Statistical Machine Translation Group

Extreme Summarization

Walid Magdy

Natural systems

Ant image
Exploring how ants navigate using visual cues

The study of natural systems can provide useful information for the development of AI, and vice versa. This can be at the level of computational neuroscience (Computational Neuroscience and Neuroinformatics) which studies how the brain processes information; inspiration for behavioural control from insects (Insect Robotics Group); computational psychiatry, which uses reinforcement learning models to understand the factors behind individual variability and clinical disorders (Peggy Series); and behavioural experiments with human subjects which can be used to underpin computational cognitive models (Joint Eyetracking Lab).

Computational Neuroscience and Neuroinformatics

Insect Robotics Group

Peggy Series

Joint Eyetracking Lab

Safety, trust, ethics of AI

It is increasingly important that designers of AI systems can provide assurances regarding properties like safety, trustworthiness, ethics and fairness. Rigorous computer-based reasoning about the agents’ models and algorithms is one route to help address these concerns (Vaishak Belle, Mathematical Reasoning Group). In order to build AI systems that people actually find useful, we need to go beyond purely technological solutions. Design Informatics (Design Informatics) connects AI specialists with designers, cognitive scientists, and human-computer interaction experts to create fair, safe, user-centric AI applications.

Vaishak Belle

Mathematical Reasoning Group

Design Informatics

AI applications

Researchers in the School develop applications of AI-based systems in a wide variety of areas. In addition to those mentioned above, these include: modelling care pathways in healthcare (Workflow FM); quantitative modelling and real-time data analysis techniques for finance and business (Tiejun Ma); and using transfer learning to study cancer evolution (Guido Sanguinetti).

Workflow FM

Tiejun Ma

Guido Sanguinetti