Shijie Chen, Ph.D.

Shijie Chen, Ph.D.

Applied operations research professional specializing in optimization and machine learning for transportation systems.

Profile

  • Background & Education
    Ph.D. in Industrial Engineering, Florida State University; Operations research specialist with 6+ years’ experience, deploying advanced analytics and optimization in transportation and logistics.

  • Core Interests
    • Transportation System Optimization (scheduling, routing, fleet sizing)
    • Predictive & Prescriptive Analytics (demand forecasting, evacuation planning)
    • Machine Learning & AI for real-time decision support
    • Multi-Modal Coordination (paratransit, last-mile logistics)
  • Technical Expertise
    • Languages & Libraries: Python (NumPy, Pandas, Scikit-learn, Keras, NLTK), SQL, MATLAB, R
    • Optimization & Solvers: GAMS, CPLEX, Gurobi
    • Data & Geospatial: GTFS, GeoPandas
    • Software Engineering: API design, Git
  • Hands-On Experience & Industrial Impact
    • $4 M+ annual operating savings (15 % cost reduction & 10 % on-time performance gain): Led the design and deployment of a nested-decomposition GAMS scheduling engine with dynamic dispatch API, integrating taxis, ride-hailing, and traditional vehicles to drive measurable cost and service improvements.
    • Enhanced baggage forecasting & cost mitigation: Collaborated with a major airline company to develop, validate and deploy flight-level checked-baggage prediction models; boost forecast accuracy and reduce mishandled-baggage costs in their operations dashboard.
    • Scalable real-time dispatch APIs: Architected and delivered production-ready dispatching and matching services for paratransit, supporting multi-modal vehicle fleets.
    • Stochastic schedule negotiation under uncertainty: Formulated a nonconvex MINLP with 17+ constraints and implemented a fix-and-optimize heuristic for real-time rider schedule adjustments—achieving a 3.5× ROI, cutting average solution time by 30 %, and increasing rider acceptance by 20 %.
  • Collaboration & Leadership
    • Cross-functional team collaboration: Engineers, planners, data scientists, and stakeholders at a transit consulting company and a paratransit operator.
    • Presented findings at INFORMS and TRB; authored 10+ peer-reviewed publications on transit optimization and machine learning.
  • Career Objectives
    Seeking a challenging role, such as Optimization Engineer, Transportation Analytics Consultant, Operations Research Lead, Data/Research/Applied Scientist, or Machine Learning Scientist, where I can leverage my blend of academic rigor and industry impact to drive measurable efficiencies and strategic insights in transportation and logistics.

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