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The world is dynamic, in constant flux. However, machine learning typically learns static models from historical data. As the world changes, these models decline in performance, sometimes
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privacy-enhancing techniques such as secure multi-party computation, homomorphic encryption, differential privacy, and trusted execution to design algorithms and protocols to secure ML models within
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The brain is a complex machine and brain function remains yet to be fully understood. This project works at the intersection of dynamical modelling, statistical signal processing, statistical
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research and cybersecurity research, notably one current direction is on advancing the latest generative AI models for cybersecurity, or vice versa: using cybersecurity to attack AI. The student is free
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Federated learning (FL) is an emerging machine learning paradium to enable distributed clients (e.g., mobile devices) to jointly train a machine learning model without pooling their raw data into a
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. This project aims to enable the widespread adoption of such tools by: Aims Creating a pre-trained model that can detect rodent body part locations in videos of common behavioural tests, without requiring manual
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models, such as ChatGPT and GPT4, incorporating the cutting-edge techniques in the other areas, such as reinforcement learning, causality and GFlowNets, to devise novel active learning algorithms for NLP
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-disciplinary team of clinician scientists and computer scientists to develop diagnosis/predictive/treatment/robotics surgery models of diseases of interest using multimodal medical data, consisting of images
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Agent-based computational simulations are now widely employed to study the evolution of behaviour, e.g., predator-prey simulations, the evolution of cooperation and altruism, the evolution of niches
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cooperating with each other, but in many cases competing for individual gains. This structure may not always work for the benefit of science. The purpose of this project is to use game theory and computational