Full Professor, East China Normal University
3 papers 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.
We propose CrossAD, a novel framework for time series Anomaly Detection that takes Cross-scale associations and Cross-window multi-scale modeling into account.
