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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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with causal inference techniques such as causal graphical models, instrumental variable analysis, and counterfactual reasoning to better handle high-dimensional, multi-environment datasets typical in
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Processing/Control Path Planning/Trajectory Planning Multi-Target Tracking/Multi-Object Tracking, Bayesian Filtering, Radom Finite Set filters or closely related multi-target tracking approaches in radar
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the evolution of massive binary stars into compact binaries as sources of gravitational-waves and astrophysical inference on gravitational-wave observations. My research group on massive binary evolution -- also
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Inference Tool (GAMBIT) Community I study various theoretical frameworks that extend the standard models of the elementary particles and cosmology to understand the nature of dark matter, dark forces and dark