Contact

For research collaboration, student supervision, and prospective student inquiries, please contact Dr. Baichuan Mo.

Email: bmo[at]tsinghua.edu.cn

Location

MoS Lab is based in Room 315, New Civil Engineering Building, Tsinghua University, Beijing, China.

Join Us

MoS Lab welcomes highly motivated students and collaborators interested in applying optimization theory and machine learning to transportation systems.

Research Directions

• Transportation system resilience: efficient optimization and machine learning algorithms to adjust operations, guide passengers, and help systems recover from incidents.
• AI for Transportation: real-time decision-making with reinforcement learning, time-series foundation models, and transportation management agents across public transit, shared mobility, and supply-chain logistics.
• Travel behavior and demand modeling: policy analysis, surveys, econometric models, and modern machine learning and optimization methods that improve traditional econometric models.
• Sustainable urban systems: commuting carbon emissions, public health risk, housing mobility, and urban cyber-physical-social system resilience.

Prospective Students

Please prepare:

• CV or resume.
• Transcript.
• Full texts of multiple representative papers, if available.
• A brief statement describing your research interests and how they connect with MoS Lab.

Please email Dr. Baichuan Mo at bmo[at]tsinghua.edu.cn with the materials above.