Ilbin Lee, PhD

Assistant Professor, Department of Accounting and Business Analytics

Contact

Assistant Professor, Department of Accounting and Business Analytics
Phone
(780) 492-7763
Address
2-29B Business Building
11203 Saskatchewan Drive NW
Edmonton AB
T6G 2R6

Overview

Research
  • Sequential decision-making
  • Data analytics
  • Health applications
  • Wildfire operations

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 2020
OM 502 - Operations Management

This course focuses on (1) the competitive advantage that a business unit can derive from innovative and efficient production and delivery of its goods and services and on (2) analytical approaches that are useful in understanding and improving an organization's operations. Specific modules include process diagramming and analysis; measuring and managing flow times; inventory control and optimization; supply chain coordination and operations strategy. Cases will be used to illustrate operational efficiency and its significance to the profitability of a firm. Prerequisite: MGTSC 501. Not to be taken by students with credit in MGTSC 502.

Winter Term 2021
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 2020
OM 710 - Individual Research

Fall Term 2020

Browse more courses taught by Ilbin Lee

Publications

Investigation Planning for Data Analysis
Author(s): Lee I., Riabov A., Sohrabi S., and Udrea O.
Publication Date: 2018
Publication: Proceedings of the 6th Goal Reasoning Workshop at IJCAI/FAIM-2018

Regularized Optimization with Spatial Coupling for Robust Decision Making
Author(s): Zheng R., Lee I., and Serban N.
Publication Date: 2018
Publication: European Journal of Operational Research
Volume: 270(3)
Issue: 3
Page Numbers: 898-906

Estimating the Cost-Savings of Preventive Dental Services Delivered to Medicaid-Enrolled Children in Six Southeastern States
Author(s): Lee I., Monahan S., Serban N., Griffin P., and Tomar S.
Publication Date: 2017
Publication: Health Services Research

Solving Large Batches of Linear Programs
Author(s): Lee I., Curry S., and Serban N.
Publication Date: 2017
Publication: INFORMS Journal on Computing

Variable Partitioning for Distributed Optimization
Author(s): Zheng R., Lee I., and Serban N.
Publication Date: 2017

Extreme Point Characterization of Constrained Nonstationary Infinite-horizon Markov Decision Processes with Finite State Space
Author(s): Lee I., Epelman M.A., Romeijn H.E, and Smith R.L.
Publication Date: 2014
Publication: Operations Research Letters
Volume: 42
Issue: 3
Page Numbers: 238-245

Simplex Algorithm for Countable-state Discounted Markov Decision Processes
Author(s): Lee I., Epelman M.A., Romeijn H.E, and Smith R.L.
Publication: Operations Research
Volume: 65
Issue: 4
Page Numbers: 1029-1042
External Link: http://pubsonline.informs.org/doi/abs/10.1287/opre.2017.1598