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We are a multi-disciplinary research group in computational and data-driven science. Our research interests are particularly oriented towards addressing challenges of scale and complexity. We are active in several areas including Decentralised AI/Federated Learning, Distributed and Cloud Computing, Cybersecurity, Machine Learning and Data-Driven Life Science.
Decentralised AI, Federated Learning
We develop methods and software to address decentralised and privacy-preserving AI. We are core contributors to the FEDn open source framework for scalable federated machine learning.
Multiscale Simulations
By adding scales to our simulations—more accurate models, incorporating some of the many complex internal structures that are vital to the function of the cell can be realised.
Cybersecurity
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Distributed Computing
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HASTE
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Learning, Inference and Optimization
The themes of learning, inference and optimization are intimately connected, and power AI and data-driven science. We build the foundations of data-efficient machine learning and optimisation. We also develop methods for performing analysis/inference with scientific data. Non-convex and global optimisation are strong focus areas in our lab.