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I supervise a wide range of projects stellar astronomy. They include modelling stars in 1D or 3D, deciphering the origin of the elements (stellar nucleosynthesis), and observing using optical
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light, focusing on controlled light-matter interactions at the nanoscale. Driven by the fascinating optical physics and photonic applications across various fields, my work spans many topical areas in
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systems, such as transformer or diffusion networks and reinforcement learning to guide the self-aware learning and network formation. As such this expected to be a purely mathematical and
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systems, such as transformer or diffusion networks and reinforcement learning to guide the self-aware learning and network formation. As such this expected to be a purely mathematical and
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. This project, from the engineering perspective, aims to explore a better development process for DL, covering requirement analysis, data collection and labeling, data cleaning, network design, training
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. This project, from the engineering perspective, aims to explore a better development process for DL, covering requirement analysis, data collection and labeling, data cleaning, network design, training
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neural networks for image, video, audio, social network, and/or text classification. The Faculty of Information Technology has a mission to advance social good through its research. Key to this mission
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neural networks for image, video, audio, social network, and/or text classification. The Faculty of Information Technology has a mission to advance social good through its research. Key to this mission
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]], pp523-560 (and here). www.doi.org: 10.1093/comjnl/bxm117 D. L. Dowe (2011a), "MML, hybrid Bayesian network graphical models, statistical consistency, invariance and uniqueness", Handbook of the
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]], pp523-560 (and here). www.doi.org: 10.1093/comjnl/bxm117 D. L. Dowe (2011a), "MML, hybrid Bayesian network graphical models, statistical consistency, invariance and uniqueness", Handbook of the