**Fall 2017-18:**

- IND ENG 172, Probability and Risk Analysis for Engineers. This is an introductory course in probability designed to develop a good understanding of uncertain phenomena and the mathematical tools used to model and analyze it. Applications will be given in such areas as reliability theory, risk theory, inventory theory, financial models, and computer science, among others. To complement the theory, the course also covers the basics of stochastic simulation.

*Lectures:*Tuesdays, Thursdays, 2:00-3:30 pm, Room: LeConte 2.

Course information and materials available to registered students via bCourses.

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

- IND ENG 173, Introduction to Stochastic Processes. 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.
- 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.