Chenxi Liu

Chenxi LIU

Assistant Professor
Centre for Artificial Intelligence and Robotics
Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences

3/F, 17W, Science Park West Avenue, Hong Kong Science Park, Hong Kong
Email: chenxi.liu@cair-cas.org.hk

Google Scholar ORCID GitHub

About Me

My name is Chenxi Liu. I am currently an Assistant Professor at Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences. Previously, I was a Postdoctoral Research Fellow at King Abdullah University of Science and Technology (KAUST), working with Prof. Panos Kalnis and a Research Fellow at Nanyang Technological University (NTU), working with Prof. Cheng Long and collaborating closely with Prof. Ziyue Li from the Technical University of Munich. I obtained my PhD at Hunan University, supervised by Prof. Dong Wang and Prof. Zhu Xiao. I was also a full-time joint PhD student at NTU. I gained valuable research experience at Zhejiang Lab, supervised by Prof. Hongyang Chen.

News
2026.09: One paper accepted by ICDE 2027.
2026.08: Three papers accepted by ACM AI Summit 2026, SIGSPATIAL 2026, and CIKM 2026.
2026.06: One paper accepted by MICCAI 2026.
2026.05: One paper accepted by ICML 2026 and one paper accepted by IJCAI 2026.
2026.04: We will organize The International Workshop on Medical Time Series Analytics and Foundation Models at ICDM 2026, Shenyang, China.
2026.01: Two papers accepted by WWW 2026 (2/2).
2025.12: Joined CAIR-HKISI-CAS as Assistant Professor.
2025.12: ST-LLM+ was selected as TKDE Popular Paper!
2025.11: GeoCRS was accepted by AAAI 2026 for Oral Presentation.
2025.11: I served as a PhD mentor at the CIKM 2025 PhD Symposium.
2025.10: Our paper accepted by TDSC.
2025.09: Joined KAUST as a Postdoctoral Research Fellow!
2025.08: We organized the tutorial LLMs Meet Cross-Modal Time Series Analytics: Overview and Directions at SSTD 2025 in Osaka, Japan.
2025.07: We organized the International Workshop on Spatio-Temporal Data Intelligence and Foundation Models at CIKM 2025, Seoul, Korea.
2025.06: Received IJCAI-25 Travel Grant.
2025.05: Gave an invited talk regarding LLMs for cross-modal time series analytics at UNSW Sydney.

Workshop

Tutorial

Publications
  • [ICDE 2027] Yuyang Zhao, Lian Xu, Hao Miao, Chenxi Liu, Hao Xue: TS-Fault: Benchmarking Time Series Forecasters Against Structural Faults. 43rd IEEE International Conference on Data Engineering, 2027.
  • [ICML 2026] Kangjia Yan*, Chenxi Liu*, Hao Miao, Xinle Wu, Yan Zhao, Chenjuan Guo, Bin Yang: Invariant Representation Learning for Source-Free Time Series Forecasting with LLM-Centric Proxy Denoising. Forty-Third International Conference on Machine Learning, 2026.
  • [AAAI 2026] Yeming Li*, Chenxi Liu*, Jie Zou, Cheng Long, Chaoning Zhang, Peng Wang, Yang Yang: From Dialogue to Destination: Geography-Aware Large Language Models with Multimodal Fusion for Conversational Recommendation. The 40th Annual AAAI Conference on Artificial Intelligenc, 2026. [Oral]
  • [TKDE 2026] Hao Miao, Ziqiao Liu, Yan Zhao, Chenxi Liu✉, Chenjuan Guo, Bin Yang, Kai Zheng, Huan Li, Christian S. Jensen: LightTR+: A Lightweight Incremental Framework for Federated Trajectory Recovery. IEEE Transactions on Knowledge and Data Engineering, 2026.
  • [WWW 2026] Yue Jiang, Chenxi Liu, Yile Chen, Qin Chao, Shuai Liu, Cheng Long, Gao Cong: Cross-city Time Series Forecasting with Retrieval-Augmented Large Language Models. The Web Conference, 2026.
  • [WWW 2026] Jinwen Chen, Hao Miao, Chenxi Liu, Yan Zhao, Kai Zheng: VisionST: Coordinating Cross-modal Traffic Prediction with Interactive Geo-image Encoding. The Web Conference, 2026.
  • [IJCAI 2026] Wanghui Qiu*, Chenxi Liu*, Shiyan Hu, Zhengyu Li, Chenjuan Guo, Bin Yang: Multi-View Ensemble for Time Series Anomaly Detection via Coupling Flows. 35th International Joint Conference on Artificial Intelligence, 2026.
  • [MICCAI 2026] Sipeng Wu, Shiqin Tang, Chenxi Liu✉, Jingwei Wu, Wenjing Jiang, Xiaoyu Xiao, Hongbin Liu, Gaofeng Meng: TRACE-PSG: TRaceable Sleep Diagnosis Support with Pointer-Grounded Evidence. The 29th International Conference on Medical Image Computing and Computer Assisted Intervention, 2026.
  • [CIKM 2026] Xuesong Zeng, Minjun Cao, Hao Miao, Chenxi Liu and Senzhang Wang: ViCO: Vision-Augmented Time Series Anomaly Detection with Prototype-Guided Coordination. 35th International ACM Conference on Knowledge and Information Management, 2026. [Oral]
  • [SIGSPATIAL 2026] Mikhail Aizinov, Wanyun Ling, Evgenii Kniazev, Chenxi Liu✉, Ziyue Li: Atmosphere-Aware Place Discovery: Semantic Urban Mapping with Multimodal Vibe Enrichment. The ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2026.
  • [ACM AI Summit 2026] Delun Kong, Wanyun Ling, Chenxi Liu, Ziyue Li: Tracing the Evidence Behind Zero-Shot Time-Series Forecasting: A Source-First Taxonomy and Audit Framework. ACM AI Leadership Summit, 2026.
  • [ESWA 2026] Hang Fan, Yunze Chai, Chenxi Liu, Weican Liu, Zuhuan Zhang, Wencai Run, Dunnan Liu. EV-STLLM: Electric Vehicle Charging Forecasting based on Spatio-temporal Large Language Models with Multi-frequency and Multi-scale Information Fusion. Expert Systems with Applications, 2026.
  • [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. The 39th Annual AAAI Conference on Artificial Intelligence, 2025. [Oral: 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. [IF: 30.4]
  • [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. 34th 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. 41st 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.
  • [TDSC 2025] Yupeng Hu, Huiling Chen, Hongrui Pan, Wenqiang Jin, Zhenyu Ye, Chenxi Liu, Kaiyi Wang: Sound Eavesdropping on Mobile Device via Audio-Induced EMR. IEEE Transactions on Dependable and Secure Computing, 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. The 25th 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 Communications, 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, CCF.
  • 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.

Invited Talks
  • May 2026, Efficient Time Series Analytics through Multi-Agent Systems, AIRS 2026, Changsha, China.
  • April 2026, Sustainable Urban Spatio-Temporal Intelligence from Deep Learning to Large Language Models, SpatialDI 2026, Changsha, China.
  • August 2025, LLMs Meet Cross-Modal Time Series Analytics: Overview and Directions, SSTD 2025, Osaka, Japan.
  • May 2025, Large Language Models Meet Cross-Modal Time Series Analytics, CRUISE, University of New South Wales, Online.

Services