Engineering (ENGR-GH)

This course presents some of the most-widely utilized tools to analyze the behavior of nonlinear dynamical systems. The first part of the course introduces the different types of equilibrium solutions of ordinary differential equations, and shows how to assess their stability, how to establish the phase-space representation of the dynamics, and to construct bifurcation diagrams and basins of attractions. The second part introduces several perturbation techniques to analytically construct approximate solutions of nonlinear ordinary differential equations. The third and final part introduces several techniques to analyze the existence and stability of periodic, quasiperiodic, and chaotic motions.

This course introduces students to the science and art of statistical model development using field and experimental data. The course is divided into three parts: 1) review of statistical inference, 2) linear regression models, and 3) models with limited dependent variables. The first component focuses on a review of statistical estimation methods, properties of estimators and hypothesis testing. The second component presents linear regression methods, with an emphasis on the statistical properties of the Ordinary Least Squares estimators under idealized conditions, and on appropriate correction methods when these conditions are violated. Systems of Linear Models are discussed with emphasis on identification. The third component extends the discussion to models with limited (discrete and censored) dependent variables, with emphasis on Logit and Probit models for categorical and ordinal data, and stochastic duration models for censored data. The course also covers models for count dependent variables, and models with discrete-continuous dependent variables. Sampling strategies are introduced.

Weekly seminar series addressing a variety of engineering topics, delivered by experts from academia and industry.

Special topics courses for graduate level engineering students.