Lijun Sun
Ph.D., P.Eng. (ON), William Dawson Scholar
Lijun Sun is an Associate Professor and William Dawson Scholar in the Department of Civil Engineering at McGill University, where he leads the Smart Transportation Lab. His research develops statistical and machine-learning methods for transportation systems that are only partially observed—incomplete passenger counts, sparse trajectories, censored demand—and for decisions that change what will be observed next. His work connects three areas that are often treated separately: inference about latent and inverse problems in transportation systems, structured uncertainty quantification, and operational decision-making under uncertainty. His group is also exploring generative AI and large language model agents for mobility reasoning, synthetic travel data and population synthesis.
Prof. Sun received his Ph.D. in Civil Engineering from the National University of Singapore (2015), conducted at the Future Cities Laboratory of the Singapore-ETH Centre, and was a Postdoctoral Associate at the MIT Media Lab (2015–2017) before joining McGill in 2018. He is Managing Editor of Artificial Intelligence for Transportation, Area Editor of Transportation Research Part C: Emerging Technologies, and Associate Editor of Transportation Science. His research has been recognized by the Best Paper Award of the TRB Standing Committee on Statistical and Econometric Methods (2024), McGill's Christophe Pierre Award for Research Excellence (2023) and the Faculty of Engineering Class of 1944 Outstanding Teaching Award (2022). He is a regular member of CIRRELT and CRM.
- Ph.D., Civil Engineering (Transportation), National University of Singapore, Singapore (2015)
- B.Eng., Civil Engineering, Tsinghua University, China (2011)
Transportation Engineering
- Meritorious Service Award, Transportation Science (2025)
- Best Paper Award, TRB Standing Committee on Statistical and Econometric Methods (AED60), Transportation Research Board (2024)
- Christophe Pierre Award for Research Excellence (Early Career), Faculty of Engineering, McGill University (2023)
- William Dawson Scholar, McGill University (2023–2027)
- Best Paper Award, 15th International Conference on Advanced Systems in Public Transport (CASPT) (2022)
- Poster Award, International Society for Bayesian Analysis (ISBA) World Meeting (2022)
- Engineering Class of 1944 Outstanding Teaching Award, Faculty of Engineering, McGill University (2022)
- Best Associate Editor Award, Transportation Research Part C: Emerging Technologies (2021)
- FRQNT New University Researchers Startup Grant (2020)
- Fellow, Chan Wui & Yunyin Rising Star Workshop (2019)
- Chen, X., Yin, Y.F., Sun, L., 2026. Inductive transportation origin-destination demand forecasting via Bayesian destination-choice modeling. Transportation Science, in press.
- Zhang, C., He, Z., Wu, C., Sun, L., 2026. When context is not enough: Modeling unexplained variability in car-following behavior. Transportation Research Part B: Methodological 214, 103588.
- Tang, Y., Qu, A., Yu, X., Deng, W., Ma, J., Zhao, J., Sun, L., 2026. From street views to urban science: Discovering road safety factors with multimodal large language models. Transportation Research Part C: Emerging Technologies 188, 105692.
- Tang, Y., Kong, M., He, J., Nie, T., Ma, W., Sun, L., 2026. LLMSynthor: Macro-aligned micro-records synthesis with large language models. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD).
- Chen, X., Cheng, Z., Sun, L., 2025. Bayesian inference of time-varying origin-destination matrices from boarding and alighting counts for transit services. Transportation Research Part B: Methodological 199, 103278.
- Lei, M., Labbe, A., Sun, L., 2025. Scalable spatiotemporally varying coefficient modeling with Bayesian kernelized tensor regression. Bayesian Analysis 20(3), 919–947.
- Zheng, V.Z., Choi, S., Sun, L., 2025. Probabilistic traffic forecasting with dynamic regression. Transportation Science 59(4), 689–707.
- Choi, S., Saunier, N., Zheng, V.Z., Trépanier, M., Sun, L., 2025. Scalable dynamic mixture model with full covariance for probabilistic traffic forecasting. Transportation Science 59(4), 708–720.
- Chen, X., Cheng, Z., Schmidt, A.M., Sun, L., 2025. Conditional forecasting of bus travel time and passenger occupancy with Bayesian Markov regime-switching vector autoregression. Transportation Research Part B: Methodological 192, 103147.
- Zheng, V.Z., Sun, L., 2024. Multivariate probabilistic time series forecasting with correlated errors. Conference on Neural Information Processing Systems (NeurIPS).
- Zhang, C., Wang, W., Sun, L., 2024. Calibrating car-following models via Bayesian dynamic regression. Transportation Research Part C: Emerging Technologies 168, 104719.
- Chen, X., Cheng, Z., Jin, J.G., Trépanier, M., Sun, L., 2023. Probabilistic forecasting of bus travel time with a Bayesian Gaussian mixture model. Transportation Science 57(6), 1516–1535.
- Wang, J., Sun, L., 2023. Robust dynamic bus control: A distributional multi-agent reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems 24(4), 4075–4088.
- Cheng, Z., Trépanier, M., Sun, L., 2022. Real-time forecasting of metro origin-destination matrices with high-order weighted dynamic mode decomposition. Transportation Science 56(4), 904–918.
- Chen, X., Sun, L., 2022. Bayesian temporal factorization for multidimensional time series prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(9), 4659–4673.
- Bayesian statistics and uncertainty quantification for transportation systems
- Spatiotemporal modelling, low-rank tensor learning and Gaussian processes
- Probabilistic forecasting of traffic, demand and travel time
- Inverse problems in transportation: origin–destination estimation, passenger flow assignment, unmet demand
- Human mobility, travel behaviour and choice modelling
- Population synthesis and generative AI for mobility (synthetic travel data, LLM agents)
- Public transport operations and planning
- Decision-making under uncertainty: Bayesian optimization, surrogate-based and stochastic optimization
- Driver behaviour and stochastic traffic simulation
- Shared and active mobility (bike-sharing, micromobility)
Editorial
- Managing Editor, Artificial Intelligence for Transportation (2025–present)
- Area Editor, Transportation Research Part C: Emerging Technologies (2025–present); Associate Editor (2020–2025)
- Associate Editor, Transportation Science (2023–present)
- Associate Editor, Data Science in Science (2025–present)
- Editorial Board Member, Journal of Public Transportation (2023–present)
Service and affiliations
- Organizer, Banff International Research Station (BIRS) workshop "Emerging Challenges in Statistical Modeling for Transportation Research" (2026)
- Senior Program Committee, AAAI Conference on Artificial Intelligence (2025–2027)
- Leadership Group, McGill Collaborative for AI and Society (McCAIS) (2025–present)
- Regular Member, Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT) (2019–present)
- Regular Member, Laboratoire de statistique, Centre de recherches mathématiques (CRM) (2023–present)
- Member, Trottier Institute for Sustainability in Engineering and Design (TISED) (2018–present)
- Member, TRB Standing Committee on Artificial Intelligence and Advanced Computing Applications (AED50) (2023–2025)
- Board Member, Chinese Overseas Transportation Association (COTA) (2022–present)
Previous positions
- Visiting Professor, School of Engineering, Sun Yat-sen University, China (2017–2018)
- Postdoctoral Associate, MIT Media Lab, Massachusetts Institute of Technology, USA (2015–2017)
- Senior Research Fellow, Future Cities Laboratory, Singapore-ETH Centre, Singapore (2014–2015)