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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
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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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achieve what neither a human being nor a machine can achieve on their own. The aim of this research is to develop cutting-edge Human-in-the-Loop Machine Learning algorithms that are able to avoid bias
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appropriate technique for their software system. In this project, we will develop AI-based methods for selecting the appropriate APR technique to fix a particular bug. The aim is to improve the effectiveness
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feasibility, and to facilitate the rapid translation of study findings into registry practice and health data environments. Project goals: The aim of the project is to develop cutting-edge AI algorithms
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will provide new models and algorithms for energy-transport integration, advancing the knowledge of mitigation strategies for sustainable urban development. #sustainability PhD student role description
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headlines around the world when a “work of art created by an algorithm” was sold at auction by Christie’s for $432,500 – nearly 45 times the value estimated before auction. It turned out that the group behind
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are based on the Cassowary algorithm, developed in part by Monash researchers. While constraint-based layout is powerful, it can be difficult for users to understand the interactions and behaviour
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Despite the popularity of providing text analysis as a service by high-tech companies, it is still challenging to develop and deploy NLP applications involving sensitive and demographic information
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develop individual-based models (also called agent-based models) to simulate insect-plant interactions. These are computer simulations where each individual animal is simulated in detail within a virtual