Erfan Rafieikia

Erfan Rafieikia

PhD Candidate
Operations and Information Systems 

Email: rafieiki@ualberta.ca

Department: Accounting and Business Analytics 
Office Hours: By appointment

Address: University of Alberta
Edmonton, AB T6G 2R6

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Research Interests

Erfan Rafieikia is a PhD candidate in Operations and Information Systems at the Alberta School of Business, University of Alberta. His research focuses on integrating optimization, machine learning, and artificial intelligence to develop scalable methods for solving complex decision-making problems under uncertainty. His work combines rigorous optimization techniques with modern machine learning to accelerate computationally intensive algorithms and improve decision quality in applications such as supply chains, retail platforms, transportation systems, healthcare, and other data-intensive operational settings. His broader research interests include large-scale optimization, data-driven optimization, and AIassisted decision-making for real-world operations management problems.

Selected Press & Features

Business PhD Research Spotlight, Alberta School of Business, May 2026

Focusing on modeling and solving large-scale decision-making problems under uncertainty, Rafieikia integrates optimization with machine learning (ML) and artificial intelligence (AI) methods. His research is driven by real-world applications in supply chain management, retail, healthcare, and transportation and logistics.

Conference Presentations

  • CORS, Kingston, Canada, 2026 (Learning-Augmented Benders Cut Generation for Large-Scale Optimization)
  • CORS, Kingston, Canada, 2026 (Strategic Integration of Delivery in Food Waste Reduction Initiatives)
  • CORS, London, Canada, 2024 (Strategic Integration of Delivery in Food Waste Reduction Initiatives)
  • ICS, Toronto, Canada, 2025 (Learning-based cut generation for convex MINLP)
  • EPSB Student AI Conference, Edmonton, Canada, 2025 (AI’s Hidden Role in Your Shopping Journey)

Honours & Awards

  • University of Alberta Three-minute Thesis (3MT) competition Finalist | 2026
  • Ranked 21st amongst 20,000 participants in the university entrance exam for graduate studies | 2018
  • Ranked 7th amongst 84 undergraduate students of Industrial Engineering at Sharif University of Technology | 2016
  • Ranked amongst the top 0.2% in the nationwide university entrance exam with around 400,000 participants | 2013