Research
Trajectory representation learning is one of my established research topics, where we aim to learn representations of trajectories (of vehicle, human, or vessel, etc.) that are universal across various downstream tasks. This also extends to building a multi-task/foundational model for trajectories that can perform various tasks at once.
UniTE: A Survey and Unified Pipeline for Pre-training Spatiotemporal Trajectory Embeddings
UVTM: Universal Vehicle Trajectory Modeling with ST Feature Domain Generation
Pre-training General Trajectory Embeddings with Maximum Multi-view Entropy Coding
Pre-training Context and Time Aware Location Embeddings from Spatial-Temporal Trajectories for User Next Location Prediction
TransferTraj: A Vehicle Trajectory Learning Model for Region and Task Transferability
NeurIPS | 2025
TrajMamba: An Efficient and Semantic-rich Vehicle Trajectory Pre-training Model
Spatiotemporal data mining is a broader topic that is inclusive of the above. It covers research on extracting and utilizing patterns from large-scale data with spatial information and that is dynamic over time (usually sourced from transportation scenarios).
KDD | 2026
RIPCN: A Road Impedance Principal Component Network for Probabilistic Traffic Flow Forecasting
PLMTrajRec: A Scalable and Generalizable Trajectory Recovery Method with Pre-trained Language Models
Path-LLM: A Multi-Modal Path Representation Learning by Aligning and Fusing with Large Language Models
DutyTTE: Deciphering Uncertainty in Origin-Destination Travel Time Estimation
Interdisciplinary research between data mining and materials science is a new research direction I am exploring. The promise is that data mining enables discovery of new materials with tailored properties, a non-trivial process in traditional materials design.
Oral Presentation | PNCS17
AMDEN: Amorphous Materials DEnoising Network
Villum Foundation
Research on Inverse Design of Materials Using Diffusion Probabilistic Models
This project focuses on developing diffusion probabilistic models to first understand the relationship between chemistry/structure and material properties, then enable the inverse design of new materials with specific properties. This project currently supports my postdoctoral research position.
AAAI | 2026