I am an Assistant Professor in the Department of Civil Engineering at McGill University. I received my PhD degree in Civil Engineering (Transportation) from National University of Singapore, and earned a Bachelor degree in Civil Engineering from Tsinghua University. During my PhD, I worked at Mobility and Transport Planning module at the Future Cities Laboratory, Singapore-ETH center. Prior to joining McGill, I was a Postdoctoral Associate at MIT Media Lab.
My current research centers on the area of smart transportation and urban computing, developing innovative methodologies and applications to address efficiency, resilience, and sustainability issues in urban transportation systems. I am particularly interested in integrating advances in machine learning into human mobility modeling, agent-based simulation, and intelligent transportation systems. My previous work has been featured in some popular media outlets, including Wired, Citylab, Scientific American, and MIT Technology Review.
For Prospective Students/Postdocs
- I am looking for one motivated Postdoc Associate to start in 2020 Fall in the general area of machine learning for transportation. Please send me an email (subject: “Prospective Postdoc Associate [Your name]”) with your CV, a brief research statement and transcripts.
- I am looking for 1-2 PhD students for 2020 Fall and 2021 Winter (Spring) who are excited about machine learning for smart transportation. If you’re interested, please send me an email or apply through the McGill uApply system. Please use “Prospective PhD student [Your name]” as your email subject.
- See more info about Civil Engineering @ McGill University.
- Ben Barres’ advice on How to Pick a Graduate Advisor.
- Philip Guo’s The Ph.D. Grind.
- If you have a good record in mathematics/machine learning, you are encouraged to apply for the IVADO graduate student scholarship.
- Postdocs or final year PhD students with strong mathematics/machine learning background (and also application in smart transportation, e.g., spatiotemporal prediction, generative model for urban activity) are strongly encouraged to apply for the IVADO postdoc scholarship. Very competitive package!
- Scholarship opportunities:
- for PhD: McGill MEDA, CSC, Quebec-China Scholarship, PBEEE Level 1 Graduate, Quebec DFW (免高奖), IVADO, McGill MEITA, 广州菁英计划;
- for Postdoc: Miatcs Elevate, NSERC Banting (best in Canada), NSERC Postdoctoral Fellowship, PBEEE Level 2 Postdoc, IVADO, CSC;
- for Undergraduate: Mitacs Globalink/international, NSERC USRA, McGill SURE.
- Urban computing & smart cities
- Machine learning for mobility modeling
- Intelligent transportation systems
- Spatio-temporal traffic state modeling/predication
- Infrastructure resilience
- Data-driven urban/transportation systems modeling
- Human mobility and travel behavior
- Agent-based modeling and simulation
- Public transportation operation & planning
- Chen, X., Sun L., 2019 Bayesian temporal factorization for multidimensional time series prediction arXiv preprint arXiv:1910.06366.
- Sun, L., Yin, Y., 2017. Discovering themes and trends in transportation research using topic modeling. Transportation Research Part C: Emerging Technologies 77, 49–66.
- Sun, L., Axhausen, K.W., 2016. Understanding urban mobility patterns with a probabilistic tensor factorization framework. Transportation Research Part B: Methodological 91, 511–524.
- Sun, L., Lu, Y., Jin, J.G., Lee, D.-H., Axhausen, K.W., 2015. An integrated Bayesian approach for passenger flow assignment in metro networks. Transportation Research Part C: Emerging Technologies 52, 116–131.
- Sun, L., Erath, A., 2015. A Bayesian network approach for population synthesis. Transportation Research Part C: Emerging Technologies 61, 49–62.
- Sun, L., Jin, J.G., Lee, D.-H., Axhausen, K.W., Erath, A., 2014. Demand-driven timetable design for metro services. Transportation Research Part C: Emerging Technologies 46, 284–299.
- Sun, L., Axhausen, K.W., Lee, D.-H., Huang, X., 2013. Understanding metropolitan patterns of daily encounters. Proceedings of the National Academy of Sciences of the United States of America 110, 13774–9.