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This project focuses on developing algorithms capable of automatically identifying and categorizing mobile ringtones. This involves leveraging machine learning techniques to analyze audio signals
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interested in connecting spatial and spectral information to understand complex materials systems at the molecular level with machine learning. PhD Student A will work with tumour sections to develop multiple
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This research project aims to address the critical need for privacy-enhancing techniques in machine learning (ML) applications, particularly in scenarios involving sensitive or confidential data
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. Scientific Contribution Our group has strong publication record of 100+ first or senior author top-tier (ERA ranking A*/A) journals and technical conferences in the machine learning and medical AI field. His
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Adversarial Machine Learning (AML) is a technique to fool a machine learning model through malicious input. Due to its significance in many scenarios, including security, privacy, and health
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With success stories ranging from speech recognition to self-driving cars, machine learning (ML) has been one of the most impactful areas of computer science. ML’s versatility stems from the wealth
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based on matched-filter statistics. Detecting the unknown relies on the development of complex algorithms at the forefront of statistics, machine learning, and data science. This multi-disciplinary
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Project description: Nowadays, data-driven machine learning algorithms are well suited to solve real-world problems that require high-level prediction accuracy. However, it seems as if nothing beats
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The world is dynamic and in a constant state of flux, yet most machine learning models learn static models from a dataset that represents a single snapshot in time. My group's research is
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On-device machine learning (ML) is rapidly gaining popularity on mobile devices. Mobile developers can use on-device ML to enable ML features at users’ mobile devices, such as face recognition