Yuxuan Liang

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 90+ papers in refereed journals (e.g., TPAMI, AI, TKDE, TMC) and conferences (such as KDD, NeurIPS, ICML, ICLR, WWW, ECCV, IJCAI, AAAI, and MM). His publications collectively gathered 4,700 citations on Google Scholar, with h-index of 34 and i10-index of 61. 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 the Assosiate Editor of Neurocomputing (IF=6.0), and PC member (or reviewer) for prestigious conferences, including KDD, ICML, ICLR, NeurIPS, WWW, CVPR, ICCV, ECCV, IJCAI, AAAI, Ubicomp and SIGSPATIAL (outstanding PC). He was recognized as Stanford/Elsevier Top 2% Scientists in 2024.

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 & Urban computing: ST representation learning, Urban/ST + LLMs, AI for social good (e.g., transportation, human mobility, environment, climate), physics-informed learning.
Time series analysis: foundation models, forecasting, imputation, decomposition, anomaly detection.
Graph mining: learning graph representations (e.g., directed graphs, temporal graphs), GNN pruning.
Multimodal learning: urban multimodal fusion, vision-language pre-training, geo-localization.

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

News

2024/09/26	Five papers on spatio-temporal graphs and time series were accepted by NeurIPS. Congrats to all!
2024/09/17	Dr. Yuxuan Liang was recognized as Stanford/Elsevier Top 2% Scientists in 2024.
2024/08/25	Our paper about spatio-temporal diffusion model was accepted by SIGSPATIAL’24. Congrats to Junfeng!
2024/07/27	Our survey on multimodel urban computing was accepted by Information Fusion (IF=14.7). Congrats to Xingchen!
2024/07/26	Our survey on last-mile delivery was accepted by TKDE. Congrats to Haomin!
2024/07/16	Our paper on urban image-text retrieval was accepted by ACM MM. Congrats to Siru!
2024/06/30	Our paper on explaining GNNs was accepted by TKDE. Congrats to Junfeng!
2024/06/20	Our paper on traffic forecasting was accepted by ECML-PKDD. Congrats to Xu!
2024/05/19	We will organize the tutorial on Foundation Models for Time Series (FM4TS) at KDD’24.
2024/05/17	Six papers on DDPM for trajectories, delivery dataset, GNNs, and TS were accepted by KDD. Congrats to all!
2024/05/06	Two papers on ST field neural network and carpark dataset were accepted by IJCAI. Congrats to all!
2024/05/01	Three papers on LLMs for time series and graph learning were accepted by ICML’24. Congrats to all!
2024/04/17	Our EdgeBrain (with Xinghai IoT) won the Silver Medal at International Exhibition of Inventions Geneva!
2024/04/17	Our paper on causal learning for trajectories was accepted by IJCAI. Congrats to Kang!
2024/04/07	Our survey on SSL for time series was accepted by TPAMI. Congrats to all collaborators!
2024/03/29	Our paper on cross-city traffic prediction was accepted by TR Part C. Congrats to Kehua!
2024/03/23	We will organize the workshop on Urban Computing (UrbComp) at KDD’24.
2024/03/12	We will organize the workshop on AI for Time Series (AI4TS) at IJCAI’24.
2024/03/10	Our paper on anomaly detection was accepted by ICDE. Congrats to Feiyi!
2024/01/23	One paper on modeling ST dynamic system was accepted by TKDE’24. Congrats to Kun and Hao!
2024/01/23	Three papers on Time Series LLM, Urban LLM, and trajectory learning were accepted by WWW’24. Congrats to all!
2024/01/16	Three papers on Time Series LLM, ST causal inference, and GLT were accepted by ICLR’24. Congrats to all!
2023/12/14	One paper on spatio-temporal causal inference was accepted by ICASSP’24. Congrats to Guorui and Prof. Liu!
2023/12/09	Three papers on spatio-temporal data and causal inference were accepted by AAAI’24. Congrats to all collaborators!
2023/12/08	Our paper on graph lottery tickets was accepted by TPAMI. Congrats to Kun!
2023/12/07	Our paper on DRL for urban sensing was accepted by ICDE. Congrats to Sijie!
2023/11/14	I was honored to be nominated as Outstanding Program Committee at SIGSPATIAL 2023.
2023/11/01	Our survey on spatio-temporal neural networks for urban computing was accepted by TKDE.
2023/10/21	Our paper about spatio-temporal causal inference was accepted by WSDM’24. Congrats to Chengxin!
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

Selected publications [See more]

* denotes corresponding author and + indicates equal contribution.

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

Bib PDF Code Cited by 519

@inproceedings{liang2018geoman,
  title = {GeoMAN: Multi-Level Attention Networks for Geo-sensory Time Series Prediction},
  author = {Liang, Yuxuan and Ke, Songyu and Zhang, Junbo and Yi, Xiuwen and Zheng, Yu},
  booktitle = {International Joint Conference on Artificial Intelligence},
  volume = {2018},
  pages = {3428--3434},
  year = {2018}
}

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

Bib PDF Code Cited by 120

@inproceedings{liang2019urbanfm,
  title = {Urbanfm: Inferring Fine-Grained Urban Flows},
  author = {Liang+, Yuxuan and Ouyang+, Kun and Jing, Lin and Ruan, Sijie and Liu, Ye and Zhang, Junbo and Rosenblum, David S and Zheng, Yu},
  booktitle = {Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery \& data mining},
  pages = {3132--3142},
  year = {2019}
}

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

Bib PDF Cited by 68

@inproceedings{liang2021fine,
  title = {Fine-grained Urban Flow Prediction},
  author = {Liang, Yuxuan and Ouyang, Kun and Sun, Junkai and Wang, Yiwei and Zhang, Junbo and Zheng, Yu and Rosenblum, David and Zimmermann, Roger},
  booktitle = {Proceedings of the Web Conference},
  pages = {1833--1845},
  year = {2021}
}

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

Bib PDF Code Cited by 487

@inproceedings{pan2019urban,
  title = {Urban Traffic Prediction from Spatio-Temporal Data using Deep Meta Learning},
  author = {Pan, Zheyi and Liang, Yuxuan and Wang, Weifeng and Yu, Yong and Zheng, Yu and Zhang, Junbo},
  booktitle = {Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery \& data mining},
  pages = {1720--1730},
  year = {2019}
}

KDD’24

[New] Foundation models for time series analysis: A tutorial and survey

Yuxuan Liang, Haomin Wen, Yuqi Nie, Yushan Jiang, Ming Jin, Dongjin Song, Shirui Pan, and Qingsong Wen*

In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Tutorial Track Paper), 2024

Bib PDF

@inproceedings{liang2024foundation,
  title = {Foundation models for time series analysis: A tutorial and survey},
  author = {Liang, Yuxuan and Wen, Haomin and Nie, Yuqi and Jiang, Yushan and Jin, Ming and Song, Dongjin and Pan, Shirui and Wen*, Qingsong},
  booktitle = {Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Tutorial Track Paper)},
  year = {2024}
}

IJCAI’24

[New] Predicting Parking Availability in Singapore with Cross-Domain Data: A New Dataset and A Data-Driven Approach

Huaiwu Zhang, Yutong Xia, Siru Zhong, Kun Wang, Zekun Tong, Qingsong Wen, Roger Zimmermann, and Yuxuan Liang*

In International Joint Conference on Artificial Intelligence, 2024

Bib PDF

@inproceedings{zhang2024predicting,
  title = {Predicting Parking Availability in Singapore with Cross-Domain Data: A New Dataset and A Data-Driven Approach},
  author = {Zhang, Huaiwu and Xia, Yutong and Zhong, Siru and Wang, Kun and Tong, Zekun and Wen, Qingsong and Zimmermann, Roger and Liang*, Yuxuan},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year = {2024}
}

WWW’24

UrbanCLIP: Learning Text-enhanced Urban Region Profiling with Contrastive Language-Image Pretraining from the Web

Yibo Yan, Haomin Wen, Siru Zhong, Wei Chen, Haodong Chen, Qingsong Wen, Roger Zimmermann, and Yuxuan Liang*

In The Web Conference, 2024

Bib PDF

@inproceedings{yan2024urbanclip,
  title = {UrbanCLIP: Learning Text-enhanced Urban Region Profiling with Contrastive Language-Image Pretraining from the Web},
  author = {Yan, Yibo and Wen, Haomin and Zhong, Siru and Chen, Wei and Chen, Haodong and Wen, Qingsong and Zimmermann, Roger and Liang*, Yuxuan},
  booktitle = {The Web Conference},
  year = {2024}
}

WWW’24

UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting

Xu Liu, Junfeng Hu, Yuan Li, Shizhe Diao, Yuxuan Liang*, Bryan Hooi, and Roger Zimmermann

In The Web Conference, 2024

Bib PDF

@inproceedings{liu2014unitime,
  title = {UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting},
  author = {Liu, Xu and Hu, Junfeng and Li, Yuan and Diao, Shizhe and Liang*, Yuxuan and Hooi, Bryan and Zimmermann, Roger},
  booktitle = {The Web Conference},
  year = {2024}
}

ICLR’24

Time-LLM: Time Series Forecasting by Reprogramming Large Language Models

Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y. Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, and Qingsong Wen

In The International Conference on Learning Representations, 2024

Bib PDF

@inproceedings{jin2024time,
  title = {Time-LLM: Time Series Forecasting by Reprogramming Large Language Models},
  author = {Jin, Ming and Wang, Shiyu and Ma, Lintao and Chu, Zhixuan and Zhang, James Y. and Shi, Xiaoming and Chen, Pin-Yu and Liang, Yuxuan and Li, Yuan-Fang and Pan, Shirui and Wen, Qingsong},
  booktitle = {The International Conference on Learning Representations},
  year = {2024}
}

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

Bib PDF Code

@inproceedings{liang2023airformer,
  title = {AirFormer: Predicting Nationwide Air Quality in China with Transformers},
  author = {Liang, Yuxuan and Xia, Yutong and Ke, Songyu and Wang, Yiwei and Wen, Qingsong and Zhang, Junbo and Zheng, Yu and Zimmermann, Roger},
  booktitle = {Thirty-Seventh AAAI Conference on Artificial Intelligence},
  year = {2023}
}

Collaborators

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.