Assistant Professor, INTR & DSA Thrust, HKUST(GZ)
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 2,200+ citations on Google Scholar, with h-index of 22 and i10-index of 32. 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.
|2023/05/17||Two papers about modeling the uncertainty or OOD in ST graphs were accepted by KDD’23.|
|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).|
|2023/02/08||One paper about contrastive learning on trajectories was accepted by ICDE’23.|
|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.|
|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.|
|2022/11 - Present||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’18GeoMAN: Multi-Level Attention Networks for Geo-sensory Time Series Prediction.In International Joint Conference on Artificial Intelligence, 2018
- KDD’19Urban Traffic Prediction from Spatio-Temporal Data using Deep Meta LearningIn Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, 2019
- IJCAI’16Urban Water Quality Prediction based on Multi-Task Multi-View LearningIn Proceedings of the 25th international joint conference on artificial intelligence, 2016
- KDD’19Urbanfm: Inferring Fine-Grained Urban FlowsIn Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, 2019
- KDD’20Nodeaug: Semi-Supervised Node Classification with Data AugmentationIn Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020
- NeurIPS’20Digraph Inception Convolutional NetworksAdvances in neural information processing systems 2020
- WWW’21Fine-grained Urban Flow PredictionIn 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
Zheyi Pan, Doctor Management Trainee Program, JD.COM
For the students I mentored, please click here.