About 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.
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)
Projects and Grants
- User-centred design and testing of a conversational Artificial Intelligence (AI) chatbot to address language barriers in Emergency Department Triage, NHMRC Ideas Grant, 2025
- CRC-P Urban CoPilot, 2025-2027
- ARC Industrial Transformation Training Centre (ITTC) for Whole Life Design of Carbon Neutral Infrastructure (DfCO2), UNSW Node Lead & Program Lead of Analytics, ARC 2024-2029
- Comprehensive Defence Data Platform, ASIC Program of Defence Trailblazer, 2024-2026
- A benchmark for sentiment and sarcasm classification for dialects of English, Google exploreCSR, Google Research, 2024
- LGBTI+ inclusion in AI, Google exploreCSR, Google Research, 2023
- ARC Centre of Excellence for Automated Decision-Making and Society (ADM+S), Co-Lead of Machines Program and Mobilities Focus area, ARC 2020-2027
- Transparent Machines, ADM+S Phase 1 project
- Explainable LLMs, ARC ADM+S PhD project
- GenAISim (Generative AI Simulation in the Loop in Hybrid Decision Making), ARC ADM+S Phase 2 project
- Aligning LLMs for Reasoning and Explanations in Reinforcement Learning, US Department of Airforce (AOARD), Airforce Research Lab (AFRL), 2024-2026
- IEA/MI2.0 Grid Integrated Control of Buildings, ICIRN, Department of Business
- Self-supervised learning of multimodal data. US Army International Technology Center Pacific (ITC-PAC), 2023-2027
- Piloting shared digital infrastructure for delivering demand flexibility and energy efficiency, CSIRO and NSW DPE-Net, Clean Tech R&D, 2023-2026
- Understanding Bias in AI Models for the Prediction of Infectious Disease Spread, CSIRO and US National Science Foundation (NSF)
- Mobility Question Answering (Q&A) for Spatio-Temporal Forecasting, Cisco Research
- IoT Data Security and Assurance Framework for Intelligent Transport, Cyber Security CRC & Cisco
- A scalable workflow for the audio monitoring of biodiversity across urban and remote Australia, Department of Climate Change, Energy, the Environment and Water
- The Digital Infrastructure Energy Flexibility (DIEF) pilot project, NSW Clean Tech R&D, Commercialisation Infrastructure Grant, NSW Government
- Towards AI on the edge, Data61 NextGen Program, CSIRO, Softbank, Aurecon
- Natural Hazard prediction and damage assessment using multimodal satellite data in self-supervised XAI model, SmartSat CRC PhD project
- Explainable RL with Counterfactuals and Causal Reasoning, US Airforce Research Lab, 2023-2027
- The Victorian Higher Education State Investment Fund (VHESIF) grant, “Digitisation for Safe Workplaces”, Victorian Government, 2021-2022
- Precinct level (or city level) energy use prediction using building data and other sources, CSIRO, 2021-2022
- INdustrial and commercial demand FLEXing to Increase Overall beNefit (INFLEXION), Gippsland Water and GWMWater, 2021-2022
- Multi-Resolution Situation Recognition for Urban-aware Smart Assistant, ARC Discovery Project, 2019-2021
- Rail Passenger Ride Comfort Modelling using In-vehicle IoT Sensor Data, TfNSW