Teaching

Graduate Student Instructor

IOE 265. (Stats 265). Probability and Statistics for Engineers (Winter 2006)
Prerequisite: Math 116 and ENG 101. I, II (4 credits)
Graphical Representation of Data; Axioms of Probability; Conditioning, Bayes Theorem; Discrete Distributions (Geometric, Binomial, Poisson); Continuous Distributions (Normal Exponential, Weibull), Point and Interval Estimation, Likelihood Functions, Test of Hypotheses for Means, Variances, and Proportions for One and Two Populations.

 
IOE 310. Introduction to Optimization Methods (Fall 2004, Winter 2005, Winter 2007)
Prerequisite: Math 214, IOE 202 and ENG 101. I, II (4 credits)
Introduction to deterministic models with emphasis on linear programming; simplex and transportation algorithms, engineering applications, relevant software. Introduction to integer, network, and dynamic programming, critical path methods.

 
IOE 316. Introduction to Markov Processes (Fall 2005)
Prerequisite: IOE 265 and Math 214. I, II (2 credits) (7-week course)
Introduction to discrete Markov Chains and continuous Markov processes, including transient and limiting behavior. The Poisson/Exponential process. Applications to reliability, maintenance, inventory, production, simple queues and other engineering problems.

 
IOE 366. Linear Statistical Models (Fall 2005)
Prerequisite: IOE 265 and Math 214. I, II (2 credits) (7-week course)
Linear statistical models and their application to engineering data analysis. Linear regression and correlation; multiple linear regression, analysis of variance, introduction to design of experiments.

 
IOE 474. Simulation (Winter 2004, Fall 2007)
Prerequisite: IOE 316, IOE 366, IOE 373. I, II (4 credits)
Simulation of complex discrete-event systems with applications in industrial and service organizations. Course topics include modeling and programming simulations in one or more high-level computer packages such as ProModel or GPSS/H; input distribution modeling; generating random numbers; statistical analysis of simulation output data. The course will contain a team simulation project.

 
IOE 510. Linear Programming I (Winter 2008)
Prerequisite: Math 217, Math 417, or Math 419. I, II, IIIa (3 credits)
Formulation of problems from the private and public sectors using the mathematical model of linear programming. Development of the simplex algorithm; duality theory and economic interpretations. Post-optimality (sensitivity) analysis application and interpretations. Introduction to transportation and assignment problems; special purpose algorithms and advance computational techniques. Students have opportunities to formulate and solve models developed from more complex case studies and to use various computer programs.

 

Sample Course Evaluations

IOE 474 Fall 2007 (midterm evaluation forms)

IOE 310 Winter 2007 (evaluation forms, sample student notes)

IOE 366 Fall 2005 (evaluation forms, sample student notes)