Intelligent Systems Laboratory
The ISL is a group of 34 core academic staff, plus associated postdoctoral researchers and PhD students, organised as a federation of five units: the Artificial Intelligence and Machine Learning Lab; the Collective Dynamics Lab; the Computational Neuroscience Unit; the Data Science Lab; and the Financial Engineering Lab.
Brief descriptions of the constituent units are:
- Artificial Intelligence and Machine Learning (AI/ML) Lab: Researchers work on machine learning, pattern recognition, bioinformatics, semantic image analysis and the study of natural intelligent systems. We are interested in the fundamental theory of learning, the design of novel algorithms and real world applications such as animal biometrics, medical bioinformatics, information extraction from data streams on the Web. and artificial musicology.
- Collective Dynamics Lab: Researchers study any of the many systems in nature and industry that consist of large numbers of constituent entities that nonlinearly interact with other entities within the system, to exhibit complex and often self-organised patterns of interaction and adaptation. Examples include swarms of animals, evolving populations of animals, the spread of epidemics, human societal interactions, and networks of interactions between companies and individuals in an economic system. The Collective Dynamics Lab addresses the question how system-level function and observable phenomena arise from the interplay of entities in these systems.
- Computational Neuroscience Unit (CNU): Researchers apply computational and mathematical approaches to the study of the brain and, in the other direction, seek to uncover insights into computation and mathematics by working with experimental neuroscientists in trying to understanding how the brain works. Our work draws inspiration from a wide range of disciplines including neuroscience, mathematics, machine learning, statistics, computer science and physics.
- Data Science Lab: Researchers work on ways to gather, wrangle, curate, visualise, analyse, and extract knowledge from the huge amounts of data we now have access to. Finding ways that enable machines to extract knowledge and insight without need for human intervention opens up whole new avenues of data exploration.
- Financial Engineering Lab (FEL): Researchers develop and apply methods from AI, Machine Learning (ML), Data Science, and advanced simulation/modelling techniques to explore and address issues in present-day and future financial systems. FEL research spans from the “micro” of creating reliably profitable adaptive automated trading systems that can operate autonomously in contemporary electronic financial markets; through intermediate-level issues such as the design of novel market mechanisms and institutions; to “macro” level issues such as modelling and predicting systemic risk and stability in entire national or international financial systems.
- Machine Learning and Computer Vision (MaVI)
Researchers aiming for excellence in fundamental machine learning and computer vision. The group has particular interests in multi-modal understanding (video, images, audio and text), long-term reasoning, robust and explainable approaches. The group is responsible for innovations in models evaluation and calibration, egocentric vision, video understanding, transferable and interoperable algorithms and machine learning for digital health.
Head of Group
Professor Dave Cliff
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