Research
Trajectory representation learning is one of my established research topics. It aims 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
Yan Lin, Zeyu Zhou, Yicheng Liu, Haochen Lv, Haomin Wen, Tianyi Li, Yushuai Li, Christian S. Jensen, Shengnan Guo, Youfang Lin, Huaiyu Wan
UVTM: Universal Vehicle Trajectory Modeling with ST Feature Domain Generation
Yan Lin, Jilin Hu, Shengnan Guo, Bin Yang, Christian S. Jensen, Youfang Lin, Huaiyu Wan
Pre-training General Trajectory Embeddings with Maximum Multi-view Entropy Coding
Yan Lin, Huaiyu Wan, Shengnan Guo, Jilin Hu, Christian S. Jensen, Youfang Lin
Pre-training Context and Time Aware Location Embeddings from Spatial-Temporal Trajectories for User Next Location Prediction
Yan Lin, Huaiyu Wan, Shengnan Guo, Youfang Lin
TransferTraj: A Vehicle Trajectory Learning Model for Region and Task Transferability
Tonglong Wei*, Yan Lin*, Zeyu Zhou, Haomin Wen, Jilin Hu, Shengnan Guo, Youfang Lin, Gao Cong, Huaiyu Wan
TrajMamba: An Efficient and Semantic-rich Vehicle Trajectory Pre-training Model
Yichen Liu*, Yan Lin*, Shengnan Guo, Zeyu Zhou, Youfang Lin, Huaiyu Wan
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).
Origin-Destination Travel Time Oracle for Map-based Services
Yan Lin, Huaiyu Wan, Jilin Hu, Shengnan Guo, Bin Yang, Christian S. Jensen, Youfang Lin
RIPCN: A Road Impedance Principal Component Network for Probabilistic Traffic Flow Forecasting
Haochen Lv*, Yan Lin*, Shengnan Guo, Xiaowei Mao, Hong Nie, Letian Gong, Youfang Lin, Huaiyu Wan
PLMTrajRec: A Scalable and Generalizable Trajectory Recovery Method with Pre-trained Language Models
Tonglong Wei*, Yan Lin*, Youfang Lin, Shengnan Guo, Jilin Hu, Haitao Yuan, Gao Cong, Huaiyu Wan
Path-LLM: A Multi-Modal Path Representation Learning by Aligning and Fusing with Large Language Models
Yongfu Wei*, Yan Lin*, Hongfan Gao, Ronghui Xu, Sean Bin Yang, Jilin Hu
DutyTTE: Deciphering Uncertainty in Origin-Destination Travel Time Estimation
Xiaowei Mao*, Yan Lin*, Shengnan Guo, Yubin Chen, Xingyu Xian, Haomin Wen, Qisen Xu, Youfang Lin, Huaiyu Wan
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.
AMDEN: Amorphous Materials DEnoising Network
AMDEN: Amorphous Materials DEnoising Network
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.