Yuxuan Liang

Assistant Professor, INTR & DSA Thrust, HKUST(GZ)
Leading the CityMind Lab


Dr. Yuxuan Liang is currently an Assistant Professor at Intelligent Transportation Thrust, also affiliated with Data Science and Analytics Thrust, Hong Kong University of Science and Technology (Guangzhou). He is working on the research, development, and innovation of spatio-temporal data mining and AI, with a broad range of applications in smart cities. Prior to that, he obtained his PhD degree at School of Computing, National University of Singapore, supervised by Prof. Roger Zimmermann and Prof. David S. Rosenblum. He also worked closely with Dr. Yu Zheng and Dr. Junbo Zhang from JD Technology. He published 40+ papers in refereed journals (e.g., AI, TKDE) and conferences (such as KDD, NeurIPS, ICLR, WWW, ECCV, IJCAI, AAAI, and MM). His publications collectively gathered 2,600+ citations on Google Scholar, with h-index of 23 and i10-index of 35. Among them, three papers (GeoMAN, ST-MetaNet and STMTMVL) were selected as the most influential IJCAI/KDD papers according to PaperDigest, which indicates their significant impacts on both industry and academia. He also served as a PC member (or reviewer) for some prestigious conferences, including KDD, ICML, ICLR, NeurIPS, WWW, CVPR, ICCV, ECCV, IJCAI, AAAI, SIGSPATIAL, and Ubicomp.

He was recognized as 1 out of 10 most innovative and impactful PhD students focusing on Data Science in Singapore by Singapore Data Science Consortium (SDSC) in 2020. His research interests mainly lie in

  • Spatio-temporal (ST) data mining: ST representation learning, time series, ST imputation, AI for social good (e.g., transportation, human mobility, environment, climate), physics-informed learning.
  • Graph mining: learning graph representations (e.g., directed graphs, temporal graphs), GNN pruning.
  • Computer vision: video understanding, image recognition, image/video super-resolution.

Useful links: CityMind Lab, Google Scholar, DBLP, Linkedin


2023/09/23 LargeST, a large-scale traffic benchmark, was accepted by NeurIPS’23 DB Track. Congrats to Xu!
2023/09/23 Our paper about spatio-temporal causal inference was accepted by NeurIPS’23. Congrats to Yutong!
2023/09/09 Our paper about spatio-temporal diffusion model was accepted by SIGSPATIAL’23. Congrats to Haomin!
2023/07/26 One paper about geospatial cross-view matching was accepted by ACM MM’23. Congrats to Wenmiao!
2023/07/15 One paper about addressing spatio-temporal heterogeneity was accepted by TMC. Congrats to Zhengyang!
2023/06/30 One paper about spatio-temporal extrapolation was accepted by TNNLS. Congrats to Junfeng!
2023/06/15 We summarized recent advances in SSL for time series in our survey paper!
2023/05/17 Two papers about learning ST graphs were accepted by KDD’23. Congrats to Junfeng and Zhengyang!
2023/03/28 We summarized recent advances in Spatio-temporal Graph Neural Networks in our survey paper!
2023/02/28 An extension of AutoSTG was accepted by Artificial Intelligence (AI). Congrats to Songyu!
2023/02/08 One paper about contrastive learning on trajectories was accepted by ICDE’23. Congrats to Yanchuan!
2023/01/25 I successfully passed my PhD defense :-) Thanks Roger and David for their continuous support at NUS!
2023/01/21 One paper about GNN pruning was accepted as poster by ICLR’23. Congrats to Kun!
2022/11/20 One paper about large-scale air quality prediction via Transformer was accepted as oral presentation by AAAI’23.
2022/11/03 One paper about learning mixed-order relationships in ST graphs was accepted by TKDE.
2022/08/23 Two paper about periodic/contrastive learning for ST data were accepted as oral papers by SIGSPATIAL’22.
2022/08/03 One paper entitled Efficient Trajectory Classification using Transformer was accepted by CIKM’22.
2022/07/29 I was honored to receive the Dean’s Graduate Award from NUS. Thanks Roger for the nomination and supports!
2022/07/04 One paper about efficient video transformer was accepted by ECCV’22.
2022/07/01 One paper about geo-orientation was accepted by ACM MM’22.
2022/05/20 One paper about sequential recommendation was accepted by KDD’22.
2022/04/15 I was awarded The 23rd China Patent Excellence Award.
2022/03/01 Three papers were accepted by NAACL’22, IEEE Access and IJCNN’22.
2022/02/24 ST-MetaNet was selected as Most Influential KDD Papers.
2022/02/24 Two spatio-temporal AI papers (GeoMAN and stMTMVL) were selected as Most Influential IJCAI Papers.

Working experience

2023/06 - Present Assistant Professor @ The Hong Kong University of Science and Technology (Guangzhou), China
2022/12 - 2023/05 Research Fellow @ National University of Singapore, Singapore
2021/07 - 2021/12 Research intern @ Sea AI Lab (SAIL), Singapore
2018/11 - 2019/04 Full-time research assistant @ National University of Singapore, Singapore
2018/01 - 2018/09 Algorithm engineer intern @ JD.COM, Beijing, China
2017/01 - 2018/01 Research intern @ Microsoft Research, Beijing, China
2015/12 - 2016/09 Research intern @ Microsoft Research, Beijing, China
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Selected publications [See more]

* denotes corresponding author and + indicates equal contribution.

  1. IJCAI’18
    GeoMAN: Multi-Level Attention Networks for Geo-sensory Time Series Prediction.
    Yuxuan Liang, Songyu Ke, Junbo Zhang, Xiuwen Yi, and Yu Zheng
    In International Joint Conference on Artificial Intelligence, 2018
  2. KDD’19
    Urban Traffic Prediction from Spatio-Temporal Data using Deep Meta Learning
    Zheyi Pan, Yuxuan Liang, Weifeng Wang, Yong Yu, Yu Zheng, and Junbo Zhang
    In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, 2019
  3. IJCAI’16
    Urban Water Quality Prediction based on Multi-Task Multi-View Learning
    Ye Liu, Yu Zheng, Yuxuan Liang, Shuming Liu, and David S Rosenblum
    In Proceedings of the 25th international joint conference on artificial intelligence, 2016
  4. KDD’19
    Urbanfm: Inferring Fine-Grained Urban Flows
    Yuxuan Liang+, Kun Ouyang+, Lin Jing, Sijie Ruan, Ye Liu, Junbo Zhang, David S Rosenblum, and Yu Zheng
    In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, 2019
  5. KDD’20
    Nodeaug: Semi-Supervised Node Classification with Data Augmentation
    Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, Juncheng Liu, and Bryan Hooi
    In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020
  6. NeurIPS’20
    Digraph Inception Convolutional Networks
    Zekun Tong, Yuxuan Liang, Changsheng Sun, Xinke Li, David Rosenblum, and Andrew Lim
    Advances in neural information processing systems 2020
  7. ICLR’23
    Searching Lottery Tickets in Graph Neural Networks: A Dual Perspective
    Kun Wang, Yuxuan Liang*, Pengkun Wang, Xu Wang, Pengfei Gu, Junfeng Fang, and Yang Wang*
    In The International Conference on Learning Representations, 2023
  8. AAAI’23
    AirFormer: Predicting Nationwide Air Quality in China with Transformers
    Yuxuan Liang, Yutong Xia, Songyu Ke, Yiwei Wang, Qingsong Wen, Junbo Zhang, Yu Zheng, and Roger Zimmermann
    In Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023
    When Do Contrastive Learning Signals Help Spatio-Temporal Graph Forecasting?
    Xu Liu+, Yuxuan Liang+, Chao Huang, Yu Zheng, Bryan Hooi, and Roger Zimmermann
    In Proceedings of the 30th International Conference on Advances in Geographic Information Systems, 2022
  10. WWW’21
    Fine-grained Urban Flow Prediction
    Yuxuan Liang, Kun Ouyang, Junkai Sun, Yiwei Wang, Junbo Zhang, Yu Zheng, David Rosenblum, and Roger Zimmermann
    In Proceedings of the Web Conference, 2021


I was really honored to closely work with the following researchers (in alphabetical order):

  • Chao Huang, Assistant Professor, University of Hong Kong

  • Kun Ouyang, Algorithm Engineer, Tencent

  • Sijie Ruan, Assistant Professor, Beijing Institute of Technology

  • Qingsong Wen, Staff Engineer/Manager, DAMO Academy, Alibaba Group (U.S.)

  • Wenmiao Hu, PhD candidate, National University of Singapore

  • Yiwei Wang, Applied Scientist, Amazon

  • Zekun Tong, National University of Singapore

  • Zhengyang Zhou, Associate Researcher, University of Science and Technology of China

  • Zheyi Pan, Doctor Management Trainee Program, JD.COM

For the students I mentored, please click here.