Siyavash Filom

Postdoctoral Fellow, DeGroote School of Business

Siyavash Filom is a mobility applied scientist and researcher specializing in freight transportation, supply chain initiatives, robust optimization, and machine-learning–driven decision support. He completed his PhD in Civil Engineering at McMaster University, where his research integrated predictive ML, reinforcement learning, and robust optimization to enable resilient and adaptive freight-network operations under uncertainty. Siyavash has contributed to major industry initiatives through the McMaster Institute for Transportation & Logistics (MITL), focusing on multimodal goods-movement analytics, Great Lakes shipping, and cross-border logistics. His work spans network optimization, Bayesian modeling, RL-based dispatching, and experimentation analytics, advancing both strategic planning and real-time decision capabilities. He is passionate about building practical, scalable products at the intersection of AI, RL, and transportation systems to deliver smarter, greener, and more adaptive mobility.

Selected Publications

• "Resilient synchromodal transport through learning assisted hybrid simulation optimization model," Transportation Research Part C: Emerging Technologies, 181, 105366.
• "A learning-based robust optimization framework for synchromodal freight transportation under uncertainty," Transportation Research Part E, 195, 103967. SSRN, 2024.
• "Applications of machine learning methods in port operations–A systematic literature review," Transportation Research Part E: Logistics and Transportation Review, 161, 102722.