Trajectory Foundation Models
I develop universal trajectory foundation models that can learn from billion-scale worldwide traces and generalize across different cities and domains.
Key Works
- UniTraj (NeurIPS 2025): A universal trajectory foundation model learned from billion-scale worldwide traces
- OmniTraj (KDD 2025): Learning generalized trajectory models from omni-semantic supervision
Research Goals
- Develop pre-trained models that can be fine-tuned for various downstream tasks
- Explore cross-city transfer learning capabilities
- Investigate efficient representation learning for large-scale trajectory data