Sean Z. Qian

Sean Z. Qian is a Professor at Carnegie Mellon University with joint appointments in the Department of Civil and Environmental Engineering (College of Engineering), Heinz College of Information Systems and Public Policy, and the Department of Electrical and Computer Engineering. He directs the Mobility Data Analytics Center (MAC) and founded the spinoff firm TraffiQure Technologies in 2020 to commercialize AI/ML technologies in infrastructure and mobility services.

His academic credentials include:

• 2012: MS in Statistics, Stanford University
• 2011: Ph.D. in Civil Engineering, University of California, Davis
• 2006: MS in Civil Engineering, Tsinghua University
• 2004: BS in Civil Engineering, Tsinghua University

Qian's research centers on large-scale dynamic network modeling and data analytics for multi-modal transportation systems, applying AI, network flow theory, and economics to address aging infrastructure challenges. His work spans Infrastructure Resilience under climate stress, Urban Systems Interdependency, and Intelligent Transportation Systems, with emphasis on sustainable network optimization and cyber-physical-social system integration. Key methodologies include remote sensing, transportation economics, and digital twin technologies for infrastructure management.

His notable scientific awards include:

• NSF CAREER Award (2018)
• Greenshields Prize, Transportation Research Board (2017)

Research funding has been secured from diverse sources:

• Federal Agencies: National Science Foundation (NSF), U.S. Department of Energy (DOE), U.S. Department of Transportation (DOT)
• State Agencies: Pennsylvania Department of Transportation (PennDOT), Maryland Department of Transportation (MDOT), Pennsylvania Department of Community and Economic Development (DCED)
• Industry Partners: IBM, Honda Research Institute, Fujitsu Inc.
• Foundations: Benedum Foundation, Hillman Foundation

As Director of the Mobility Data Analytics Center (MAC), Qian leads collaborations with Fujitsu on digital twin technologies for infrastructure management in southwestern Pennsylvania, while actively mentoring graduate students and recruiting Ph.D. candidates with strong quantitative backgrounds for Fall 2025.