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We are interested in combining Machine Learning and Human-Computer Interaction to improve health, wellness and livability in smart cities with interpretable predictive analytics and mobile apps for automated self-tracking.
Research methods include:
User-centered design of technologies driven by deep requirement analysis with traditional and sensor-based methods.
Development of new technologies by defining frameworks, and developing toolkits and platforms for rapid prototyping of AI- and machine learning-driven applications.
Implementation of hardware sensors to acquire context-awareness of users and the environment, machine learning models to interpret higher level semantics, and intelligible user interfaces and visualizations to provide effective insights and services.
Validation of real-world applications to evaluate technologies in lab and field studies.
Research methods include:
User-centered design of technologies driven by deep requirement analysis with traditional and sensor-based methods.
Development of new technologies by defining frameworks, and developing toolkits and platforms for rapid prototyping of AI- and machine learning-driven applications.
Implementation of hardware sensors to acquire context-awareness of users and the environment, machine learning models to interpret higher level semantics, and intelligible user interfaces and visualizations to provide effective insights and services.
Validation of real-world applications to evaluate technologies in lab and field studies.
研究兴趣
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arxiv(2023)
arxiv(2023)
Mario Michelessa,Christophe Hurter,Brian Y. Lim, Jamie Ng Suat Ling,Bogdan Cautis,Carol Anne Hargreaves
BIG DATA AND COGNITIVE COMPUTINGno. 3 (2023): 149-149
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