Borzou Rostami, PhD

Area of Study / Keywords

Large-scale Optimization Data-driven Decision Making Machine Learning Applied to Real-time Decision Making Integration of Machine Learning and Optimization Supply Chain and Retails Analytics Transportation and Logistics Smart Cities

---

About

Borzou Rostami is an assistant professor and the CPA chair of Business Analytics at the Alberta School of Business, University of Alberta. He is also a member of the Canada Excellence Research Chair in Data Science for Real-Time Decision-Making (CERC DS4DM) at Polytechnique Montreal and Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT). Borzou holds a PhD in Information Technology/Operations Research from the Polytechnic University of Milan, Italy. Before joining the U of A, Borzou was an assistant professor at Wilfrid Laurier University and a postdoctoral researcher at Polytechnique Montreal and TU Dortmund, Germany. Borzou is currently an Associate Editor for INFOR (Information Systems and Operational Research). His research interests focus on developing optimization methods (integrated with machine learning algorithms) for large-scale decision making under data uncertainty that arise in supply chain, retail, health care, and transportation and logistics. 

MMA 600 - Coding Bootcamp

Two-Week Kick Start Bootcamp: Embark on a seamless learning journey as students engage in a well-rounded experience to master two essential programming languages - Python and R. Restricted to students registered in the MMA Program. Non-MMA students require consent of home dept and the Masters Programs Office.

---

MMA 601 - Business Foundations and Strategic Decision Making

Students are introduced to business fundamentals in the first session followed by second session that delves deep into the dynamic world of data-driven strategy, cultivating invaluable skills in utilizing data to frame decisions effectively. Restricted to students registered in the MMA Program. Non-MMA students require consent of home dept and the Masters Programs Office.

---

MMA 602 - Machine Learning For Business I

The goal of the Machine Learning for Business course is to utilize machine learning techniques to transform raw data into valuable insights that can inform business strategies. This course demands a solid grasp of technical data handling methods as well as business goals. It involves an overview of various machine learning approaches, such as supervised and unsupervised learning, and their practical uses in business scenarios. Restricted to students registered in the MMA Program. Non-MMA students require consent of home dept and the Masters Programs Office.

---

MMA 607 - Prescriptive Analytics

This course is designed to provide a foundation of prescriptive analytics based on mathematical modeling and optimization for managerial decision-making. Topics covered in the course include decision analysis; simulation modeling; constraint programming and constraint-based optimization; network optimization and graph algorithms; optimization under uncertainty; application of prescriptive analytics techniques in various industries; integration of predictive and prescriptive analytics; and practical implementation of prescriptive analytics techniques to solve real-world problems. By the end of the course, students will have a solid understanding of prescriptive analytics techniques and their practical applications. Restricted to students registered in the MMA Program. Non-MMA students require consent of home dept and the Masters Programs Office.

---

OM 423 - PRESCRIPTIVE ANALYTICS

Prescriptive analytics involves the use of data, mathematical models, and algorithms to identify optimal solutions for achieving organizational goals. This process builds on descriptive and predictive analytics, going beyond the interpretation of past events and the forecasting of future scenarios to also provide advice on the most effective actions to meet business objectives. Students acquire the skills to convert complex business problems into mathematical models, and employ Python programming and commercial solvers to derive optimal decisions. Evaluation components will consist of assignments, case studies, group projects, and two midterm exams. Prerequisites: OM 252 or 352

---

OM 623 - Prescriptive Analytics

Prescriptive analytics involves the use of data, mathematical models, and algorithms to identify optimal solutions for achieving organizational goals. This process builds on descriptive and predictive analytics, going beyond the interpretation of past events and the forecasting of future scenarios to also provide advice on the most effective actions to meet business objectives. Students acquire the skills to convert complex business problems into mathematical models, and employ Python programming and commercial solvers to derive optimal decisions. Evaluation components will consist of assignments, case studies, group projects, and two midterm exams. Prerequisites: OM 502.

---

OM 702 - Advanced Research Topics in Operations Management

This course will provide an in-depth introduction to a particular methodology or a particular setting that is relevant to research in operations management. The topic may vary from year to year. Possible topics include optimization modeling and formulation, stochastic modeling and optimization, behavioural research in operations management, and health care operations management. The required background for students will vary depending on the topic. This course may be appropriate for some graduate students in engineering or computing science. Prerequisite: Written permission of the instructor. Approval of the Business PhD Program Director is also required for non-PhD students.

---

Browse more courses taught by Borzou Rostami

Bayani M., Rostami B., Yossiri A., Rousseau L.M.

Informs Journal on Computing. 2024 March; 10.1287/ijoc.2021.0186

---

Babaee S., Araghi M., Rostami B.

Transportation Research Part B: Methodological. 2022 October; 164 10.1016/j.trb.2022.08.005

---

Babaee S., Araghi M., Castillo I., Rostami B.

International Journal of Production Research. 2022 August; 10.1080/00207543.2022.2104179

---

Rostami, B., Errico, F. and Lodi, A

Operations Research. 2022 February; 71 (2):471-486 10.1287/opre.2021.2241

---

Haughton, M., Rostami, B. and Espahbod, S.

EURO Journal on Transportation and Logistics. 2022 January; 10.1016/j.ejtl.2022.100071

---

Rostami, B., Chitsaz, M., Arslan, O., Laporte, G. and Lodi, A.

Operations Research. 2021 December; 10.1287/opre.2021.2185

---

Rostami, B., Desaulniers, G., Errico, F. and Lodi, A.

Operations Research. 2021 February; 10.1287/opre.2020.2037

---

Rostami, B., Kämmerling, N., Naoum-Sawaya, J., Buchheim, C. and Clausen, U.

European Journal of Operational Research. 2020 August; 10.1016/j.ejor.2020.07.051

---

Rostami, B., Kämmerling, N., Buchheim, C. and Clausen, U.

Computers & Operations Research. 2018 April; 10.1016/j.cor.2018.04.002

---

Rostami, B., Chassein, A., Hopf, M., Frey, D., Buchheim, C., Malucelli, F. and Goerigk, M.

European Journal of Operational Research. 2018 February; 10.1016/j.ejor.2018.01.054

---

Rostami, B. and Malucelli, F.

Electronic Notes in Discrete Mathematics. 2016 November; 10.1016/j.endm.2016.10.020

---

Rostami, B., Strothmann, C. and Buchheim, C.

Lecture Notes in Computer Science. 2016 September; 10.1007/978-3-319-45587-7_21

---

Rostami, B., Buchheim, C., Meier, J.F. and Clausen, U.

Electronic Notes in Discrete Mathematics. 2016 May; 10.1016/j.endm.2016.03.010

---

Meier, J.F., Clausen, U., Rostami, B. and Buchheim, C.

Electronic Notes in Discrete Mathematics. 2016 May; 10.1016/j.endm.2016.03.006

---

Rostami, B., Malucelli, F., Belotti, P. and Gualandi, S.

European Journal of Operational Research. 2016 March; 10.1016/j.ejor.2016.03.031

---

Rostami, B., Malucelli, F., Frey, D. and Buchheim, C.

Lecture Notes in Computer Science. 2015 June; 10.1007/978-3-319-20086-6_29

---

Rostami, B. and Malucelli, F.

Computers & Operations Research. 2015 June; 10.1016/j.cor.2015.06.005

---

Rostami, B. and Malucelli, F.

Discrete Optimization. 2014 September; 10.1016/j.disopt.2014.08.003

---

Rostami, B., Cremonesi, P. and Malucelli, F.

Studies in Computational Intelligence. 2013 December; 10.1007/978-3-319-00410-5_2

---

View additional publications