Chenxi LIU
Research Fellow
College of Computing and Data Science, Nanyang Technological University
ABN-02b-11, Academic Block North, 61 Nanyang Dr, Singapore

Email: chenxi.liu@ntu.edu.sg; cxliu@hnu.edu.cn

[Google Scholar] [ResearchGate] [ORCID] [GitHub]

About me | Publications | Awards | Book | Services

About Me

I am currently a research fellow at College of Computing and Data Science (CCDS), Nanyang Technological University (NTU), supervised by Prof. Cheng Long and collaborate closely with Prof. Ziyue Li from the University of Cologne. Previously, I completed my PhD at the College of Computer Science and Electronic Engineering, Hunan University (HNU), under the supervision of Prof. Dong Wang and Prof. Zhu Xiao. Additionally, I gained valuable research experience through an internship at Zhejiang Lab, supervised by Prof. Hongyang Chen.

My research interests focus on time series analytics, large language models, and multimodal learning.

Tutorial
  • [SSTD 2025] Chenxi Liu, Hao Miao, Cheng Long, Yan Zhao, Ziyue Li, Panos Kalnis: LLMs Meet Cross-Modal Time Series Analytics: Overview and Directions. 19th International Symposium on Spatial and Temporal Data, 2025.

Publications
  • [AAAI 2025] Chenxi Liu, Qianxiong Xu, Hao Miao, Sun Yang, Lingzheng Zhang, Cheng Long, Ziyue Li, Rui Zhao: TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignment. Annual AAAI Conference on Artificial Intelligence, 2025. [Oral Presentation: Top 4.6%]
  • [CSUR 2025] Chenxi Liu, Zhu Xiao, Wangchen Long, Tong Li, Hongbo Jiang, Keqin Li: Vehicle Trajectory Processing, Analytics, and Applications: A Survey. ACM Computing Surveys, 2025. [Impact Factor: 28]
  • [TKDE 2025] Chenxi Liu, Kethmi Hirushini Hettige, Qianxiong Xu, Cheng Long, Shili Xiang, Gao Cong, Ziyue Li, Rui Zhao: ST-LLM+: Graph Enhanced Spatio-Temporal Large Language Models for Traffic Prediction. IEEE Transactions on Knowledge and Data Engineering, 2025.
  • [IJCAI 2025] Chenxi Liu, Shaowen Zhou, Qianxiong Xu, Hao Miao, Cheng Long, Ziyue Li, Rui Zhao: Towards Cross-Modality Modeling for Time Series Analytics: A Survey in the LLM Era. International Joint Conference on Artificial Intelligence, 2025.
  • [ICDE 2025] Chenxi Liu, Hao Miao, Qianxiong Xu, Shaowen Zhou, Cheng Long, Yan Zhao, Ziyue Li, Rui Zhao: Efficient Multivariate Time Series Forecasting via Calibrated Language Models with Privileged Knowledge Distillation. IEEE International Conference on Data Engineering, 2025.
  • [T-ITS 2025] Chenxi Liu, Zhu Xiao, Cheng Long, Dong Wang, Tao Li, Hongbo Jiang: MVCAR: Multi-View Collaborative Graph Network for Private Car Carbon Emission Prediction. IEEE Transactions on Intelligent Transportation Systems, 2025.
  • [MDM 2024] Chenxi Liu, Sun Yang, Qianxiong Xu, Zhishuai Li, Cheng Long, Ziyue Li, Rui Zhao: Spatial-Temporal Large Language Model for Traffic Prediction. IEEE International Conference on Mobile Data Management, 2024. [🏆 Top-1 Most Cited Paper among Accepted Papers]
  • [ICDE 2024] Ziqiao Liu, Hao Miao, Yan Zhao, Chenxi Liu, Kai Zheng, Huan Li: LightTR: A Lightweight Framework for Federated Trajectory Recovery. IEEE International Conference on Data Engineering, 2024.
  • [DESA 2024] Sun Yang, Qiong Su, Zhishuai Li, Ziyue Li, Hangyu Mao, Chenxi Liu, Rui Zhao: SQL-to-Schema Enhances Schema Linking in Text-to-SQL. International Conference on Database and Expert Systems Applications, 2024.
  • [WWWJ 2024] Jiawei Cai, Dong Wang, Hongyang Chen, Chenxi Liu, Zhu Xiao: Modeling Dynamic Spatiotemporal User Preference for Location Prediction: A Mutually Enhanced Method. World Wide Web, 2024.
  • [WWWJ 2023] Chenxi Liu, Zhu Xiao, Dong Wang, Minhao Cheng, Hongyang Chen, Jiawei Cai: Foreseeing Private Car Transfer between Urban Regions with Multiple Graph-based Generative Adversarial Networks. World Wide Web, 2023.
  • [TNSE 2022] Chenxi Liu, Zhu Xiao, Dong Wang, Lei Wang, Hongbo Jiang, Jiangxia Yu: Exploiting Spatiotemporal Correlations of Arrive-Stay-Leave Behaviors for Private Car Flow Prediction. IEEE Transactions on Network Science and Engineering, 2022.
  • [JSEN 2021] Chenxi Liu, Jiawei Cai, Dong Wang, Jiaxin Tang, Lei Wang, Huiling Chen, Zhu Xiao: Understanding the Regular Travel Behavior of Private Vehicles: An Empirical Evaluation and A Semi-supervised Model. IEEE Sensors Journal, 2021.
  • [T-ITS 2021] Jianhua Xiao, Zhu Xiao, Dong Wang, Vincent Havyarimana, Chenxi Liu, Chengming Zhou, Di Wu: Vehicle Trajectory Interpolation Based on Ensemble Transfer Regression. IEEE Transactions on Intelligent Transportation Systems, 2021.
  • [JOC 2021] Chenxi Liu, Dong Wang, Huiling Chen, Renfa Li: Forecasting Urban Private Car Volumes based on Multi-source Heterogeneous Data Fusion. Journal on Communication, 2021.
  • [ISCI 2021] Guangyin Jin, Chenxi Liu, Zhexu Xi, Hengyu Sha, Yanyun Liu, Jincai Huang: Adaptive Dual-View WaveNet for Urban Spatial-temporal Event Prediction. Information Sciences, 2021.
  • [HPCC 2020] Huiling Chen, Dong Wang, Chenxi Liu: Towards Semantic Travel Behavior Prediction for Private Car Users. IEEE International Conference on High Performance Computing and Communications, 2020.

Awards
  • 2025, WiEST Development Grant. Women in Engineering, Science, and Technology, NTU.
  • 2025, IJCAI-25 Travel Grant, International Joint Conferences on Artificial Intelligence.
  • 2025, KDD Commendable Reviewer Recognition, Applied Data Science (ADS) Track.
  • 2024, SoBigData Award for Diversity and Inclusion, European Union.
  • 2024, DDSA Visit Grant, Danish Data Science Academy.
  • 2021, China National Computer Congress (CNCC) Travel Grant, Rank A, China Computer Federation.
  • 2018, Outstanding Graduates in Sichuan Province, Department of Education of Sichuan Province, China.
  • 2017, National Scholarship, Ministry of Education.

Book
  • 2022, Practical Applications of Deep Learning in Traffic Big Data, Tsinghua University Press (ISBN: 978-7-302-60292-7)
    • Authored Chapter 9, which provides an exploration of practical approaches to private vehicle trajectory data mining.

Talk
  • May 2025, Large Language Models Meet Cross-Modal Time Series Analytics at CRUISE.

Services

Visitors