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people management skills will be complemented by a deep understanding of statutory reporting, financial control, and system improvement. Strong analytical, auditing, and problem-solving abilities
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Project Background & Motivation Active learning (AL) mitigates the heavy annotation costs of deep learning by strategically querying the most informative unlabeled samples. However, traditional AL
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/learning-with-privacy-at-scale [3] Fang et al., "Local Model Poisoning Attacks to Byzantine-Robust Federated Learning". In USENIX Security Symposium, 2020. [4] Zhu et al., "Deep Leakage from Gradients
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Discovery Project, this research aims to develop highly novel physics-informed deep learning methods for Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) and applications in image
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Distribution Drift Data-Efficient Deep Learning for De Novo Molecular Design from Analytical Spectra Authorised by: Marketing, Faculty of IT , Monash University . Maintained by: Marketing, Faculty of IT
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deep learning techniques. This project will systematically review existing work and explore the development and evaluation of automated approaches for alert validation and attack investigation, with
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analysis, or multi-omics integration, with strong competence in deep learning frameworks (e.g., PyTorch/TensorFlow) and data engineering for reproducible research. Familiarity with cloud/HPC workflows
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addition to audio. Candidates will be expected to devise novel multi-modal generation models by incorporating ideas and techniques from various techniques, such as causality, deep learning, deep reinforcement
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Education Bayesian Uncertainty Estimation for Robust Single- and Multi-View Learning in CV and NLP Robust Active Learning Under Distribution Drift Data-Efficient Deep Learning for De Novo Molecular Design
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Active Learning Under Distribution Drift Data-Efficient Deep Learning for De Novo Molecular Design from Analytical Spectra Authorised by: Marketing, Faculty of IT , Monash University . Maintained by