Ilbin Lee, PhD - Directory@UAlberta.ca

Contact

Associate Professor, Alberta School of Business - Department of Accounting and Business Analytics

Email: ilbin@ualberta.ca
Phone: (780) 492-7763
Address: 3-21 C Business Building
11203 Saskatchewan Drive NW

Edmonton AB

T6G 2R6

Overview

Area of Study / Keywords

Decision-making Data uncertainty Data analytics Wildfire operations Healthcare Reinforcement learning Markov decision processes Policymaking

Research

Selected Research Papers

Lee, I. (2023) Is Separately Modeling Subpopulations Beneficial for Sequential Decision-Making? Operations Research. Davis, S., Zhang, J., Lee, I., Rezaei, M., Greiner, R., McAlister, F., & Padwal, R. (2022). Effective Hospital Readmission Prediction Models using Machine-Learned Features. BMC Health Services Research, 22, 1415.
Zheng, Y., Xie, Y., Lee, I., Dehghanian, A., & Serban, N. (2022). Parallel Subgradient Algorithm with Block Dual Decomposition for Large-scale Optimization. European Journal of Operational Research, 299(1), 60–74.
Curry, S., Lee, I., Ma, S., & Serban, N. (2022). Global Sensitivity Analysis via a Statistical Tolerance Approach. European Journal of Operational Research, 296(1), 44–59.
Lee, I., Curry, S., & Serban, N. (2019). Solving Large Batches of Linear Programs. INFORMS Journal on Computing, 31(2), 302–317.
Lee, I., Monahan, S., Serban, N., Griffin, P., & Tomar, S. (2018). Estimating the Cost Savings of Preventive Dental Services Delivered to Medicaid-Enrolled Children in Six Southeastern States. Health Services Research, 53(5), 3592–3616.

Selected Presentations

Retirement Decision-Making: When is the Right Time?
– INFORMS Annual Meeting, October 2024
Data-Driven Predictive and Prescriptive Approach for Wildfire Suppression Resource Deployment: A Case Study of Alberta Wildfires
– INFORMS Annual Meeting, October 2023
The Curse of Passive Data Collection in Batch Reinforcement Learning
– CORS/INFORMS International Conference, June 2022
Estimating the Treatment Effect of Initial Attack Resources in Wildfire Suppression
– Wildland Fire Canada Conference, November 2022
– POMS Conference, April 2022
Effective Hospital Readmission Prediction Models using Machine-Learned Features
– CORS Conference, May 2023
– INFORMS Healthcare Conference, July 2021
– INFORMS Annual Meeting, November 2020
Is Separately Modeling Subpopulations Beneficial for Sequential Decision-Making?
– INFORMS Annual Meeting, October 2022
– MSOM Conference, June 2022
– ESCP Business School, May 2022
– Industrial and Systems Engineering, KAIST, December 2019
– Industrial Management Engineering, Korea University, December 2019
– Management Science, University of Waterloo, November 2019
– INFORMS Healthcare Conference, July 2019
– INFORMS Computing Society Conference, January 2019
– Sauder School of Business, University of British Columbia, October 2018

Announcements

A Spotlight on Research at the Alberta School of Business

What does the 'Markov decision process' have to do with call centres?

My findings tell us...

A new algorithm can improve firm solutions.
Firms can minimize overall labour and waiting time costs using this new algorithm.
This new algorithm finds a 'rule' that is simple to implement resulting in optimal staffing levels.

Read more about this research...

Courses

OM 420 - Predictive Business Analytics

Application of predictive statistical models in areas such as insurance risk management, credit risk evaluation, targeted advertising, appointment scheduling, hotel and airline overbooking, and fraud detection. Students will learn how to extract data from relational databases, prepare the data for analysis, and build basic predictive models using data mining software. Emphasizes the practical use of analytical tools to improve decisions rather than algorithm details. Prerequisite: MGTSC 352 or OM 352.

Fall Term 2025 Winter Term 2026

OM 620 - Predictive Business Analytics

Application of predictive statistical models in areas such as insurance risk management, credit risk evaluation, targeted advertising, appointment scheduling, hotel and airline overbooking, and fraud detection. Students will learn how to extract data from relational databases, prepare the data for analysis, and build basic predictive models using data mining software. Emphasizes the practical use of analytical tools to improve decisions rather than algorithm details. Prerequisite: MGTSC 501.

Fall Term 2025 Winter Term 2026

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