Collaborative Human-Centric AI Systems (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 in our CRUISE GitHub repository.
CRUISE RESEARCH CAPABILITIES
- Machine Learning; Unsupervised Learning; Deep Learning; Adversarial Learning
- Time-series, Spatio-temporal Data
- Analytics and Forecasting
- Prediction + Optimization
- Natural Language Processing
- Mobility data science
- On-device AI; Edge and federated learning
- Behaviour modelling
- Mobile and wearable sensing and AI
- Personalization and profiling
- Activity recognition, emotion recognition
- Recommender systems
- Responsible, Ethical, Equitable AI (Fairness, Debiasing, Transparency, Explainability)
