01
Spatio-Temporal Data Mining
Representation learning, similarity modeling, retrieval, and prediction for trajectories, events, and other structured spatio-temporal data.
Zhichen Lai 赖志宸
I'm a Researcher.
About
Zhichen Lai (赖志宸) is an Associate Research Professor in the College of Computer Science and Big Data, Fuzhou University, China, and a member of the Fujian Key Laboratory of Network Computing and Intelligent Information Processing. He was selected for the China's Ministry of Education Overseas Talent Program (教育部海外引才计划) in 2025. He currently leads the Data Intelligence and Interdisciplinary Information Group (DIG@FZU).
He received his Ph.D. in Computer Science from Aalborg University (AAU), Denmark, in early 2025, where he also served as a Research Fellow. At AAU, he was advised by MAE and ACM/IEEE Fellow Professor Christian S. Jensen, Dalin Zhang, Huan Li, and Hua Lu.
He was also a Junior Visiting Research Fellow at the University of New South Wales (UNSW) in 2024. He received his M.S. in Computer Science from Sichuan University (SCU) in 2021 under the supervision of Prof. Jiancheng Lv, and his B.S. in Computer Science from the University of Electronic Science and Technology of China (UESTC) in 2018.
Research Areas
02
Geospatial Artificial Intelligence
Data-driven methods for understanding geographic processes, human mobility, and place-based patterns across space and time.
03
Efficient Deep Learning
Resource-aware model design, training, and inference for deep learning systems that must be both effective and deployable.
04
AI for Scientific and Healthcare Applications
Machine learning for scientific and healthcare data, including interdisciplinary applications in dental medicine and environmental systems.
Representative Publications
01
AAAI 2026
Oral (Top 1.2%)
CCF-A
MovSemCL: Movement-Semantics Contrastive Learning for Trajectory Similarity
2026
02
IEEE TKDE
CCF-A
CAS Q1
JCR Q1
Lightweight Correlated Time Series Forecasting Enhanced with Model Distillation
2025
03
SIGKDD 2024
Oral
CCF-A
ReCTSi: Resource-efficient Correlated Time Series Imputation via Decoupled Pattern Learning and Completeness-aware Attentions
2024
04
WWW 2024
Oral (Top 9.4%)
CCF-A
E2USD: Efficient-yet-effective Unsupervised State Detection for Multivariate Time Series
2024
05
SIGMOD 2023
Oral
CCF-A
LightCTS: A Lightweight Framework for Correlated Time Series Forecasting
2023
Honors
China's Ministry of Education Overseas Talent Program
Qishan Scholar Program (Overseas Talent Introduction), Fuzhou University
Organizations
Associate Research Professor
Ph.D. in Computer Science & Research Fellow
Junior Visiting Research Fellow
M.S. in Computer Science
B.S. in Computer Science
High School · Science Track
Prospective Students
I welcome inquiries from prospective master's students and first- or second-year undergraduate students interested in research.
Master's capacity for 2026 is currently full.
What I Value
- Initiative Curiosity, self-motivation, and the habit of working through problems independently.
- Programming A solid coding background; experience with machine learning or deep learning is a plus.
- Communication Clear communication in both Chinese and English, and a willingness to discuss ideas openly.
- Ambition An interest in careful, high-quality work; students considering a Ph.D. are especially welcome.
You do not need to be strong in every dimension. Commitment, consistency, and growth matter more than a polished CV.
What You Can Expect
- Focus Projects are developed jointly within the group's broader agenda, with room for new ideas and initiative.
- Mentorship Close guidance on coding, experiments, reading, and academic writing, with the goal of helping you become independent quickly.
- Support Research can be difficult at times. I encourage early communication and steady problem-solving rather than silent struggle.
- Environment The group provides solid resources and a supportive environment, and I am happy to advise students considering graduate study or a longer research path.
The goal is simple: that your time in the group is demanding, productive, and ultimately worth it.
Contact
zhla ατ cs.aau.dk
Please replace “ατ” with “@” when writing.
Office
Room 302, Building No. 2, College of Computer Science and Big Data, Fuzhou University, Minhou, Fuzhou, Fujian, China