Planet Formation at Solar System Scales with the James Webb Space Telescope. Planetary systems like our own form within vast disks of primordial gas and dust around newborn stars. This project will observe such disks spanning a range of ages with the James Webb Space Telescope to reveal the detailed in-situ physics of planet-forming disks themselves. We will deliver the sharpest-ever infrared images in astronomy, exploiting the only Australian-designed instrument on the spacecraft: the Aperture ....Planet Formation at Solar System Scales with the James Webb Space Telescope. Planetary systems like our own form within vast disks of primordial gas and dust around newborn stars. This project will observe such disks spanning a range of ages with the James Webb Space Telescope to reveal the detailed in-situ physics of planet-forming disks themselves. We will deliver the sharpest-ever infrared images in astronomy, exploiting the only Australian-designed instrument on the spacecraft: the Aperture Masking Interferometer. This yields new physics for actively growing protoplanets, carved rings and gaps in disks, and gravitationally sculpted patterns of leftover cometary debris. Confronting state-of-the-art models with these data will immediately yield profound insights into planetary system formation, including our own.Read moreRead less
Data Complexity and Uncertainty-Resilient Deep Variational Learning. Enterprise data present increasingly significant characteristics and complexities, such as multi-aspect, heterogeneous and hierarchical features and interactions, and evolving dependencies and multi-distributions. They continue to significantly challenge the state-of-the-art probabilistic and neural learning systems with limited to insufficient capabilities and capacity. This research aims to develop a theory of flexible deep v ....Data Complexity and Uncertainty-Resilient Deep Variational Learning. Enterprise data present increasingly significant characteristics and complexities, such as multi-aspect, heterogeneous and hierarchical features and interactions, and evolving dependencies and multi-distributions. They continue to significantly challenge the state-of-the-art probabilistic and neural learning systems with limited to insufficient capabilities and capacity. This research aims to develop a theory of flexible deep variational learning transforming new deep probabilistic models with flexible variational neural mechanisms for analytically explainable, complexity-resilient analytics of real-life data. The outcomes are expected to fill important knowledge gaps and lift critical innovation competencies in wide domains.Read moreRead less