Nooshin-Salari

Research

Our research focuses on advancing data-driven decision-making and optimization techniques for large-scale, complex systems. We apply cutting-edge methods, including reinforcement learning, and develop innovative models and algorithms to tackle challenges in areas such as supply chain management, urban mobility, smart city services, and healthcare systems. Our work is committed to driving efficiency, sustainability, and innovation across diverse industries and urban environments.

Bike sharing systems

Healthcare planning

Facility location

Supply chain management

Recent Publications:

Liu, S., & Salari, N., "Fairness in accessibility of public service facilities", Operations Research Letters, 55, 107141 (2024).
Carlsson, J. G., Liu, S., Salari, N., & Yu, H., "Provably good region partitioning for on-time last-mile delivery", Operations Research, 72(1), 91-109 (2024).
Azizi, F., & Salari, N., "A novel condition-based maintenance framework for parallel manufacturing systems based on bivariate birth/birth–death processes", Reliability Engineering & System Safety, 229, 108798 (2023).
Salari, N., Liu, S., & Shen, Z. J. M.,"Real-time delivery time forecasting and promising in online retailing: When will your package arrive?", Manufacturing & Service Operations Management, 24(3), 1421-1436 (2022).

Google Scholar

Our Team

Nooshin Salari

Assistant Professor

Postdoctoral Fellows and Researchers

Maryam
Motamedi

Postdoctoral Fellow

Sana
Arastefar

Research Assistant

Graduate Students

Mohammadreza
Eslamipirharati

Ph.D. Student

Mina
Kanani Torshizi

Master Student

Undergraduate Students

Yibo
Wang

Teaching

Operations Management, O611 (Winter 2025, Summer 2025)
Offered as part of the MBA program at McMaster University, this course focuses on process improvement across both service sectors—such as healthcare, financial services, and restaurant management—and manufacturing industries.

Engineering Economic Analysis, ENG M 620 (Winter 2024)
Offered at the University of Alberta, this course provides students with a comprehensive understanding of the fundamentals of engineering economics, financial analysis, and market assessment. It emphasizes the application of these principles to formulate, evaluate, and select optimal engineering alternatives in the planning, development, and management of industrial enterprises.

Engineering Project Management, ENG M 530 ( Fall 2022, Winter 2023)
Offered at the University of Alberta, this course introduces key project management tools, techniques, templates, and methodologies. It equips students with practical skills applicable across various engineering projects, while examining the eight knowledge areas outlined by the Project Management Institute (PMI).

Methods of Quality Control and Improvement, MIE364 (Fall 2019, Fall 2021, Fall 2022)
Offered at the University of Toronto, this course introduces students to statistical methods for monitoring and controlling processes, along with quality improvement techniques using designed experiments. These techniques are applicable across a range of engineering disciplines.

Data-Driven Optimization & Operations Research (D-DOOR)