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 100+ papers in refereed journals (e.g., TPAMI, AI, TKDE, TMC, IJGIS) and conferences (such as KDD, NeurIPS, ICML, ICLR, WWW, ECCV, IJCAI, AAAI, and MM). His publications collectively gathered 7,100+ citations on Google Scholar, with h-index of 43. Among them, six papers (UrbanCLIP, UnTime, MixupGNN, GeoMAN, ST-MetaNet and STMTMVL) were selected as the most influential papers according to PaperDigest, which indicates their significant impacts on both industry and academia. He has served as the Assosiate Editor of Neurocomputing (IF=6.0), and Area Chair/Senior PC for prestigious conferences, including KDD, NeurIPS, IJCAI and ICASSP.

Based on his contributions to spatio-temporal data mining and urban computing, 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. He has received several awards both domestically and internationally, including SDSC Dissertation Research Fellowship, the 23rd China Patent Excellence Award, and the ACM SIGSPATIAL China Chapter Rising Star Nomination Award. He is currently leading projects funded by the National Natural Science Foundation of China (NSFC), Guangdong Province General Projects, CCF Tencent Rhino-Bird Fund, and CCF Didi Gaia Fund, among others.

His research interests mainly lie in

Spatio-temporal (ST) data mining: ST foundation models, Urban/ST + LLMs, AI for social good (e.g., urban, transportation, human mobility, environment, climate), physics-informed learning.
Urban computing: urban data sensing, urban data management, urban data analytics, urban decision-making.
Time series analysis: foundation models, forecasting, imputation, decomposition, anomaly detection.
Multimodal learning: urban multimodal fusion, vision-language pre-training, geo-localization.

Other Useful links: CityMind Lab, Google Scholar, DBLP, Linkedin, Official Page

News

2025/04/10	Welcome to submit a tutoiral proposal to SSTD’25! Any topic related to ST data is encouraged :)
2025/03/15	We will organize the Urbcomp’25 and MiLeTS Workshop at KDD’25. The website will open soon.
2025/03/12	Yuxuan will serve as the Area Chair for KDD’25, NeurIPS’25, MM’25 and SPC for IJCAI’25.
2025/02/14	We will organize a workshop and a tutorial on AI for Time Series at WWW’25.
2025/02/14	We will organize a workshop on Spatio-Temporal Data Mining at WWW’25.
2025/02/14	We will organize a tutorial on Human Mobility Analytics at WWW’25.
2025/02/10	13 papers were accepted by ICLR’25, WWW’25, KDD’25 and AAAI’25. Congrats to all!
2024/09/17	Yuxuan received the ACM SIGSPATIAL China Chapter Rising Star Nomination Award.
2024/09/17	Yuxuan was recognized as Stanford/Elsevier Top 2% Scientists in 2024.

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

Awards

Several selected awards are listed below:

ACM SIGSPATIAL China Rising Star Nomination Award, 2024
Stanford/Elsevier Top 2% Scientists, 2024
Silver Medal at International Exhibition of Inventions Geneva, 2024
Outstanding Program Committee at SIGSPATIAL 2023
Dean's Graduate Research Excellence Award Award, NUS, 2022.
The 23rd China Patent Excellence Award, 2022.
SDSC Dissertation Research Fellowship, 2020 (1 out of 10 most innovative and impactful PhD students focusing on Data Science in Singapore.)
Research Scholarship at NUS: 2019-2023
Outstanding Winners of The Interdisciplinary Contest in Modeling (ICM), top 0.2% (19 winners in 9773 groups), 2015
China Computer Federation (CCF) Elite Collegiate Award, 2015
National Scholarship (four times)

Selected publications [See more]

* denotes corresponding author and + indicates equal contribution.

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 Cited by 69

@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 Cited by 95

@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}
}

KDD’24

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 Cited by 130

@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}
}

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 Cited by 125

@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}
}

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 86

@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 614

@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’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 150

@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}
}

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 594

@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}
}