PhD student, The Hong Kong University of Science and Technology (Guangzhou)
1 paper at NeurIPS 2025
We propose FactoST, a two-stage spatio-temporal foundation model that factorizes universal temporal pretraining from spatio-temporal adaptation, enabling efficient and generalizable forecasting across domains.
