Ilbin Lee, PhD

Area of Study / Keywords

Data-driven decision-making Data analytics Reinforcement learning Markov decision processes Wildfire operations Healthcare

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Research

Selected Research Papers

Lee, I. (2024) Is Separately Modeling Subpopulations Beneficial for Sequential Decision-Making? Operations Research, 72(6), 2595-2611. 

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.

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...

BUAN 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: OM 252. Not open to students with previous credit in OM 420.

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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.

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Browse more courses taught by Ilbin Lee

I. Lee

Operations Research. 2024 November; 72 (6):2595-2611 10.1287/opre.2023.2474

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Davis S., Rezaei M., Lee I., Zhang J., Greiner R., Padwal R., and McAlister F.

BMC Health Services Research. 2022 November; 22 10.1186/s12913-022-08748-y

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European Journal of Operational Research. 2022 January; 10.1016/j.ejor.2021.04.004

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European Journal of Operational Research. 2022 January; 10.1016/j.ejor.2021.11.054

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Proceedings of Machine Learning Research. 2022 January;

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INFORMS Journal on Computing. 2019 January; 10.1287/ijoc.2018.0838

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Zheng R., Lee I., and Serban N.

European Journal of Operational Research. 2018 January; 270(3) (3):898-906

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Lee I., Monahan S., Serban N., Griffin P., and Tomar S.

Health Services Research. 2017 January;

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Lee I., Epelman M.A., Romeijn H.E, and Smith R.L.

Operations Research Letters. 2014 January; 42 (3):238-245

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Lee I., Epelman M.A., Romeijn H.E, and Smith R.L.

Operations Research. 65 (4):1029-1042

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