Teaching at Northeastern University
IE 7215 Simulation Analysis (Fall 2018-present)
Elementary queueing models, simulation model design, simulation languages
Input data analysis and distribution fitting, model verification and validation, output
analysis and transient/steady-state response
Terminating/nonterminating systems, model experimentation and optimization
Random number/random variate generation, and variance reduction techniques
IE4510 Simulation Modeling Analysis (Fall 2018-present)
Process model design and development, validation, and experimentation for discrete-event simulation models
Topics include problem formulation, data collection and analysis, random-variable generation, model development,
scenario experimentation, statistical analysis of output, and resultant decision management.
Utilizes a major industry-standard simulation software application with animation capabilities.
IE 7280
Statistical Methods in Engineering (Fall 2023-present)
Students learn how to build, interpret, and apply predictive models such as model selection, regularization, bias-variance tradeoff, extrapolation.
Students learn how some linear and nonparametric methods work and why they work, when they should be concerned about various issues, which methods are well-suited for certain situations, and the extent of the conclusions that can be drawn from various models.
Students learn how to apply this knowledge to practical problems, and how to use statistical software, e.g. R, to analyze data.
IE 6200 Engineering Probability and Statistics(Fall 2024-present)
This is an introductory course to statistics and students learn to collect data, summarize and display data, then further draw conclusions from data.
Students learn basic probability to apply in statistical analysis in class.
Students learn point estimation, confidence intervals, hypotheses tests.
Teaching at RPI
ISYE 4290/6620 Discrete-Event Simulation Modeling (Fall 2017)
Introduction to discrete-event simulation modeling and analysis techniques including; graphical simulation
modeling approaches, animation techniques, modeling large-scale and complex systems, pseudo-random number
and random variate generation, stochastic processes.
Input modeling (data collection, analysis, and
fitting distribution), output analysis (initial bias and termination bias, variance reduction techniques),
sensitivity analysis, design of experiments, interactive simulation-based decision-support systems.
ISYE 4210/6600 Design of Manufacturing Systems and Supply Chains (Spring 2016, 2017)
Dynamics of manufacturing systems and supply chains, lean manufacturing, lead time reduction in
manufacturing and service operations, advanced pull systems, concurrent design of products
and supply chains, and integration of information technology in supply chain operations.
Analysis of models and
their application to design and planning problems in manufacturing as well as service systems is emphasized.
ISYE 4140 Statistical Analysis (Fall 2014, 2015)
Review of simple and multiple regression, selection procedures, regression diagnostics, residual analysis,
stepwise regression, analysis of variance, design of experiments including factorial experiments,
analysis of ordinal data and nonparametric inference, basic time series models.
Extensive use of
statistical software. Emphasis on statistical applications to industrial engineering.
Co-teaching at Northwestern University
IMC 451 Statistics and Marketing Research (Fall 2013)
