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CRUISE
Collaborative Human-Centric AI Systems (CRUISE) Lab, led by Prof. Flora Salim, works on machine learning for time-series, spatio-temporal data, and multimodal sensor data, and on trustworthy AI (including fairness, explainability, mechanistic interpretablity) for decision making systems. Our research is supported by the ARC, CRC, and many local and international industry and government partners. We share our codes and some sample datasets in our CRUISE GitHub repository.
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ZARA: Zero-shot Motion Time-Series Analysis via Knowledge and Retrieval Driven LLM Agents
Paper • 2508.04038 • Published • 1 -
SensorLLM: Human-Intuitive Alignment of Multivariate Sensor Data with LLMs for Activity Recognition
Paper • 2410.10624 • Published • 1 -
COMODO: Cross-Modal Video-to-IMU Distillation for Efficient Egocentric Human Activity Recognition
Paper • 2503.07259 • Published -
zechenli03/sensor_qa_data
Updated • 8 • 1
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ZARA: Zero-shot Motion Time-Series Analysis via Knowledge and Retrieval Driven LLM Agents
Paper • 2508.04038 • Published • 1 -
SensorLLM: Human-Intuitive Alignment of Multivariate Sensor Data with LLMs for Activity Recognition
Paper • 2410.10624 • Published • 1 -
COMODO: Cross-Modal Video-to-IMU Distillation for Efficient Egocentric Human Activity Recognition
Paper • 2503.07259 • Published -
zechenli03/sensor_qa_data
Updated • 8 • 1
models
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datasets
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CRUISEResearchGroup/Massive-STEPS-Beijing
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CRUISEResearchGroup/Massive-STEPS-Tokyo
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CRUISEResearchGroup/Massive-STEPS-Sydney
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CRUISEResearchGroup/Massive-STEPS-Shanghai
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CRUISEResearchGroup/Massive-STEPS-Sao-Paulo
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CRUISEResearchGroup/Massive-STEPS-Petaling-Jaya
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CRUISEResearchGroup/Massive-STEPS-New-York
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CRUISEResearchGroup/Massive-STEPS-Moscow
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CRUISEResearchGroup/Massive-STEPS-Melbourne
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CRUISEResearchGroup/Massive-STEPS-Jakarta
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