BIOGRAPHY

Dr. Farhangdoost is a data scientist and researcher whose work and interests include machine learning applications, operations research, and sequential decision modelling. He earned his Ph.D. in Analytics from Queen’s University’s Smith School of Business, where his dissertation investigated the integration of expert-informed decisions with machine learning through sequential decision models in the context of financial crimes compliance risk management. 

Before joining MRU as an Assistant Professor of Statistics & Analytics, Alireza spent over five years collaborating with industry partners as a graduate research fellow at Scotiabank Centre for Customer Analytics. He later joined Scotiabank’s AI Research & Special Projects team as a data scientist, where he worked full-time to turn his ideas into production-ready solutions at the bank. For the past year, Alireza has been working as a data scientist at Helcim, a financial technology company based in Calgary, where he focuses on building and implementing machine learning models for real-time payment processing and customer risk management.

Teaching experience & Curriculum Design

Statistics & Analytics for Business (MGMT 2262)

Business Stats for Decision Making (MGMT 2263)

RESEARCH AND SCHOLARLY WORK

Azaron, Amir, Uday Venkatadri, and Alireza Farhang Doost. "Designing profitable and responsive supply chains under uncertainty." International Journal of Production Research 59.1 (2021): 213-225.