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Advanced strongly typed languages like Haskell and emerging type systems like refinement types (as implemented in Liquid Haskell) offer strong guarantees about the correctness of programs. However, when type errors occur it can be difficult for programmers to understand their cause. Such...
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In NeuroDistSys (NDS): Optimized Distributed Training and Inference on Large-Scale Distributed Systems, we aim to design and implement cutting-edge techniques to optimize the training and inference
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learning by using Bayesian learning principles. Among other things, Bayesian learning gives AI systems the ability to quantitatively express a degree of belief about a prediction or statement. By bridging
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The relationship between the information-theoretic Bayesian minimum message length (MML) principle and the notion of Solomonoff-Kolmogorov complexity from algorithmic information theory (Wallace and
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for inference, yet differs from standard Bayesian approaches through its information-theoretic foundation. The MML87 approximation achieves computational tractability while remaining virtually identical to Strict
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motif, hence renders the identification of the binding protein difficult. Here we propose for the first time to apply the Bayesian information-theoretic Minimum Message Length (MML) principle to optimise
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plants they visit and pollinate. Bayesian networks (BNs), and other probabilistic graphical models, can provide a visual representation of the underlying structure of a complex system by representing
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nodes and chemical bonds as edges. Analysis these networks are important as they may provide AI-based approaches for drug discovery. This project will focus on representing and inferring chemical or
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This PhD project is funded by a successful ARC Discovery Project grant: "Improving human reasoning with causal Bayesian networks: a user-centric, multimodal, interactive approach" and the successful
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engineering and a strong foundation in data science. You bring a passion for solving complex problems and a track record of research excellence in optoelectronic materials, machine learning, or related fields