# Courses

**Spring 2019-20:**

- STOR 445, Introduction to Stochastic Modeling. This is an introductory course in stochastic models. Topics include: discrete-time Markov chains, Poisson process, continuous-time Markov chains, and renewal theory, with applications to queueing theory, risk analysis and reliability theory.

*Lectures:*Tuesdays and Thursdays, Hanes 120, 2:00 – 3:15 pm. - STOR 672, Simulation Modeling and Analysis. This is a graduate level course in stochastic simulation, focusing on the simulation of univariate distributions, discrete-event simulation models, output analysis, and Markov chain Monte Carlo methods. The course combines both theory and implementation, and requires familiarity with a programming language such as Matlab, Python or R.

*Lectures:*Tuesdays and Thursday, Hanes 130, 12:30 – 1:45 pm.

Course information and materials available to registered students via Sakai.

**Courses taught at UNC Chapel Hill (2018 – present):**

- STOR 305, Decision-making using spreadsheet models. This course covers a broad set of topics in Operations Research, including linear programming, non-linear programming, decision analysis, and Monte Carlo simulation, focusing on problems that can be solved using Excel spreadsheets.
- STOR 445, Introduction to Stochastic Modeling. This is an introductory course in stochastic models. Topics include: discrete-time Markov chains, Poisson process, continuous-time Markov chains, and renewal theory, with applications to queueing theory, risk analysis and reliability theory.

**Courses taught at UC Berkeley (2016-2018):**

- IND ENG 172, Probability and Risk Analysis for Engineers. This is an introductory course on probability at the undergraduate level.
- IND ENG 173, Introduction to Stochastic Processes.
- IND ENG 150, Production Systems Analysis. Quantitative models for operational and tactical decision making in production systems, including production planning, inventory control, forecasting, and scheduling.

**Courses taught at Columbia University (2006-2016):**

- IEOR 3600, Introduction to Probability and Statistics. This is an introductory course in probability and statistics designed to develop a good understanding of uncertain phenomena and the mathematical tools used to model, analyze, and validate hypothesis.
- IEOR 3658, Probability. This is an introductory course on probability at the undergraduate level.
- IEOR 3106 Intro to OR: Stochastic Models. This is an undergraduate level course on stochastic models. Topics include: the Poisson process, renewal theory, discrete and continuous time Markov chains.
- IEOR 4404, Simulation. This course covers the basics of discrete event simulation, and is intended for master’s students and senior undergraduates with a good background in probability. A course on stochastic processes is recommended, but not a requirement.
- IEOR 6711, Stochastic Models I. Advanced treatment of stochastic modeling in the context of queueing, reliability, manufacturing, insurance risk, financial engineering and other engineering applications. Review of elements of probability theory; exponential distribution; renewal theory; Wald’s equation; Poisson processes. Introduction to both discrete and continuous-time Markov chains.
- IEOR 8100, Random Graphs. This is an introductory PhD level course on random graph theory. The topics include the classical Erdos-Renyi, preferential attachment and configuration models, as well as some recent results and generalizations. We will discuss applications to social networks and the WWW, among others.
- IEOR 8100, Branching Processes and Applications. This is an introductory PhD-level course to the theory and applications of branching processes. The course is designed to build up from basic probability and stochastic processes, and is therefore suitable for first year PhD students or advanced master students interested in the topic.
- IEOR 8100, Large Deviations: Applications in OR. This is a PhD level course on large deviations with applications to queueing theory.