Lijun Sun is a Postdoctoral Associate at MIT Media Lab. His current research focuses on developing and applying crowdsourcing and data-driven approaches in the domain of civil systems and transportation. Before that, he worked at Future Cities Laboratory, Singapore-ETH Centre as a PhD student and then research fellow in the Mobility and Transportation Planning Module, combining smart card-driven public transport modeling and agent-based simulation to improve transit service quality and reliability. He holds a Ph.D. in Civil Engineering from National University of Singapore.
His research interests include data-driven transport modeling, mobility and travel behavior profiling, urban computing and urban complexity, computational social science and large-scale agent-based modeling/simulation. His research aims to provide a better understanding of urban and transportation systems and how scalable cooperation and artificial intelligence could benefit human society. His work has been featured in popular media outlets, including Wired, Citylab, Scientific American and MIT Technology Review.
National University of Singapore
2011 - 2015
PhD in Transportation (Civil & Environmental Engineering) Working at the Mobility & Transportation Planning Module, Future Cities Laboratory Advisors: Prof. Kay W. Axhausen, Prof. Der-Horng Lee Committee: Prof. Qiang Meng, Prof. Mi Diao Graduate thesis: Research on urban transit reliability using smart card data
2007 - 2011
BEng in Civil Engineering Undergraduate thesis: Research on micro-simulation models of mixed traffic flow
Data-driven transport modeling
I focus on understanding various unexplored properties of transport systems by untilizing new data sources
Human mobility and travel behavior
I am very interested in understanding ourselves - the mobility and behavior patterns of human beings with the help of big data
Urban dynamics & Urban complexity
I want to explore the impact of the urban evolution/revolution and the nature behind various critical urban phenomena.
I am interested in implementing large-scale agent-based transport simulations
I try to use data to provide better public transport services (bus, metro & taxi)