I am a researcher in computer science, with my established research centered around data mining, i.e., extracting and utilizing patterns from large-scale data, usually with machine learning models. Computer science is also one of my hobbies, and I am interested in most topics relevant to it, especially SysOps and full-stack development.

I am building my teaching experience, where my foundational understanding of the subject matter comes from both my research experience and the knowledge I gained during my hobbyist computer science exploration.

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.

IEEE TKDE | 2025
Paper Preprint Code
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

IEEE TKDE | 2025
Paper Preprint Code
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

IEEE TKDE | 2023
Code Paper Preprint
Pre-training General Trajectory Embeddings with Maximum Multi-view Entropy Coding

Yan Lin, Huaiyu Wan, Shengnan Guo, Jilin Hu, Christian S. Jensen, Youfang Lin

AAAI | 2021
Paper Code
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

NeurIPS | 2025
Preprint Oral Poster
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

NeurIPS | 2025
Poster
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).

SIGMOD | 2024
Paper Preprint Code
Origin-Destination Travel Time Oracle for Map-based Services

Yan Lin, Huaiyu Wan, Jilin Hu, Shengnan Guo, Bin Yang, Christian S. Jensen, Youfang Lin

KDD | 2026
Preprint
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

NeurIPS | 2025
Poster Preprint
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

WWW | 2025
Paper Code
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

AAAI | 2025
Preprint Paper
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

NeurIPS | 2024
Paper Poster
Mobility-LLM: Learning Visiting Intentions and Travel Preference from Human Mobility Data with Large Language Models

Letian Gong*, Yan Lin*, Xinyue Zhang, Yiwen Lu, Xuedi Han, Yichen Liu, Shengnan Guo, 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.

Workshop | NeurIPS 2025
Paper
AMDEN: Amorphous Materials DEnoising Network
Oral Presentation | PNCS17
Slides
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.

AAAI | 2026
Paper Preprint
SculptDrug: A Spatial Condition-Aware Bayesian Flow Model for Structure-based Drug Design

Qingsong Zhong, Haomin Yu, Yan Lin, Wangmeng Shen, Long Zeng, Jilin Hu

Teaching

My course teaching follows three core principles: a solid understanding of the subject matter, awareness of students' established experiences and background, and problem-based learning style applied throughout lectures, exercises, and exams. This translates to practices including tailor-made blog-style literature that is both professional and intuitive, inspirational lectures that connect theory to real-world problems, and progressively-arranged hands-on exercises that build practical problem-solving skills.

Fall 2025 | Aalborg University
Literature
AI Systems & Infrastructure

This course introduces students to streamlined interaction with AI models and systems, as well as implementation and deployment of scalable, production-ready AI systems on real-world infrastructures.

I am building my supervision experience, currently supervising Bachelor's-level semester projects. My supervision focuses on problem definition, critical thinking about design choices, and delivering convincing conclusions.

Fall 2025 - Spring 2026 | Aalborg University
Proposal
A Camera That Sees Behind

A Bachelor's semester project on developing a computationally lightweight model deployable on embedded devices to remove humans from images or video feeds, serving as a prototype GDPR-compliant surveillance system.

Service

I actively contribute to the academic community through professional membership and peer review of journal and conference papers.

  • IEEE, ACM member
  • Secretary of IEEE (Denmark Section) Computer Society
  • Reviewer for journals: TKDE, TKDD, TNNLS, TIST, TII, and TVT
  • Member of program committees of conferences: KDD, ICLR, NeurIPS, AAAI, CVPR, ICCV, IJCAI, WWW, and WACV