Artificial Intelligence for Urban Transport Planning; Spatiotemporal Intelligence for Human Mobility Modeling; Big Data and Travel Behavior Analysis; Sustainable and Future Mobility Systems

Education Experience

2020-2024，The University of Hong Kong, PhD in Urban Planning and Design

2023-2024，MIT Senseable City Lab, Visiting PhD Student

2018-2020，Tsinghua University, Master in Architecture

2014-2018，Tsinghua University, Bachelor in Architecture

Professional Experience

2025-，Tsinghua University，Assistant Professor，PhD Supervisor

2024-2025，Singapore-MIT Alliance for Research and Technology，Postdoc Associate

Teaching

40000123 Urban Planning and Design

Selected Journal Papers

† co-first author; * corresponding author.

[1] Liang, Y., Wang, S.*, Yu, J., Zhao, Z., Zhao, J., Pentland, S. (2025). Analyzing sequential activity and travel decisions with interpretable deep inverse reinforcement learning. Travel Behaviour and Society, accepted in Oct 2025.

[2] He, M., Liang, Y.*, Wang, S., Zheng, Y., Wang, Q., Zhuang, D., Tian, L., Zhao, J. 2025. Human-guided urban form generation using multimodal diffusion models. Building and Environment, 113892.

[3] Tang, Y., Zhao, Z., Deng, W., Lei, S., Liang, Y., Ma, Z. 2025. RouteKG: A knowledge graph-based framework for route prediction on road networks. IEEE Transactions on Intelligent Transportation Systems.

[4] Wang, Q., Liang, Y., Zheng, Y., Xu, K., Zhao, J. and Wang, S.* 2025. Generative AI for Urban Planning: Synthesizing Satellite Imagery via Diffusion Models. Computers, Environment and Urban Systems, 122, 102339.

[5] Qiao, Q., Ren, C., Chen, S., Liang, Y., Lai, Y., Zhou, Y., Schuldenfrei, E. *, Sarkar, C., Webster, C., 2025. Architectural design and building-level infections during the early stage of COVID-19: A study of 2597 public housing in Hong Kong. Building and Environment, accepted in March 2025.

[6] Liang, Y., Zhao, Z*., Ding, F., Tang, Y. and He, Z., 2024. Time-aware trip generation for bike sharing planning: A multi-task memory-augmented graph neural network. Information Fusion, p.102294.

[7] Liang, Y., Liu, Y., Wang, X. and Zhao, Z. *, 2024. Exploring large language models for human mobility prediction under public events. Computers, Environment and Urban Systems, accepted in July 2024.

[8] Liang, Y., Zhao, Z. *, Webster, C. J., 2024. Generating sparse origin-destination flows on shared mobility networks using probabilistic graph neural networks. Sustainable Cities and Society, 114: 105777.

[9] Liang, Y., Zhao, Z. * and Zhang, X., 2024. Modeling taxi cruising time based on multi-source data: A case study in Shanghai. Transportation, 51(3): 761-790.

[10] Feng, J. *, Liang, Y., Hao, Q. and Xu, K., and Qiu, W., 2024. Comparing effectiveness of point-of-interest data and land use data in theft crime modelling: a case study in Beijing. Land Use Policy, 147: 107357.

[11] Liang, Y., Huang, G. and Zhao, Z. *, 2023. Cross-mode knowledge adaptation for bike sharing demand prediction using domain-adversarial graph neural networks. IEEE Transactions on Intelligent Transportation Systems, 25(5): 3642-3653.

[12] Huang, G., Liang, Y. and Zhao, Z. *, 2023. Understanding market competition between transportation network companies using big data. Transportation Research Part A: Policy and Practice, 178, p.103861.

[13] Liang, Y., Ding, F., Huang, G. and Zhao, Z. *, 2023. Deep trip generation with graph neural networks for bike sharing system expansion. Transportation Research Part C: Emerging Technologies, 154, p.104241.

[14] Zhao, Z. †* and Liang, Y. †, 2023. A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewards. Transportation Research Part C: Emerging Technologies, 149, p.104079.

[15] Liang, Y., Zhao, Z.* and Sun, L., 2022. Memory-augmented dynamic graph convolution networks for traffic data imputation with diverse missing patterns. Transportation Research Part C: Emerging Technologies, 143, p.103826.

[16] Liang, Y., Huang, G. and Zhao, Z.*, 2022. Joint demand prediction for multimodal systems: A multi-task multi-relational spatiotemporal graph neural network approach. Transportation Research Part C: Emerging Technologies, 140, p.103731.

[17] Liang, Y. and Zhao, Z.*, 2020. Nettraj: A network-based vehicle trajectory prediction model with directional representation and spatiotemporal attention mechanisms. IEEE Transactions on Intelligent Transportation Systems, 23(9), pp.14470-14481.

Selected Conference Papers

[1] Ding, F., Liang, Y., Wang, Y., Yang, Y., Zhou., Y., Zhao, Z.*, 2024. A graph deep learning model for station ridership prediction in expanding metro networks. In Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Urban-AI, Atlanta, GA.

[2] Liang, Y., Ding, F., Tang, Y. and Zhao, Z.*, 2023. Time-aware trip generation for bike sharing system planning. In 12th ACM SIGKDD International Workshop on Urban Computing (UrbComp’23), Long Beach, CA, USA. 

[3] Liang, Y., Ding, F., Huang, G. and Zhao, Z.*, 2023. Predicting potential demand for bike sharing system expansion using a multi-graph attention network. In 16th World Conference on Transport Research (WCTR), Montreal, Canada.

[4] Liang, Y., Huang, G. and Zhao, Z.*, 2022. Bike sharing demand prediction based on knowledge sharing across modes: A graph-based deep learning approach. In IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) (pp. 857-862), Macao, China.

Selected Awards

HKU Li Ka Shing Prize

AI and Cities: An International Forum for Innovation and Collaboration-Outstanding Research Award

HK-Swiss Symposium on Future Cities-Best Presentation Award

HKU Presidential PhD Scholarship

HKU Foundation Publication Award for Research Postgraduate Students

First Prize, Chengyuan Cup - Planning Decision Support Model Design Contest (Group Member)

Beijing Outstanding Undergraduate