ME 6973 Adv Reliability Methods

  • Extreme value theory

  • Random fields

  • Sampling-based reliability analysis methods including Monte Carlo simulation

  • Response surface development

  • Random processes

  • System reliability

ME 5013 - Advanced Data Analytics

  • Data visualization: Multivariate, hierarchical, temporal, and network data visualization

  • Regression and regularization: Linear regression, logistic regression, regularization, ridge regression, nonparametric regression with Gaussian processes

  •  Classification basics: Loss functions, naive Bayes, linear classifiers

  • Support vector machines, convex optimization

  • Kernels: Model selection, cross validation

  • Ensemble methods: Boosting, bagging, random forest

  • Dimension reduction: principal component analysis

  • Clustering, mixture models, EM algorithms

  • Bayesian Inference, sampling algorithms, MCMC

  • Stochastic processes: Markov models, hidden Markov models

  • Graphical Models: State space models, Kalman filter

EGR 5213 Introduction to Modelling and Simulation

  • Preliminaries

    • Random variable generation

    • Markov Chain

  • Monte Carlo Methods

    • Monte Carlo Integration

    • Monte Carlo Optimization

    • Markov Chain Monte Carlo

    • Convergence

  • Discrete Event Simulation

    • Simulation concepts

    • Modeling basic operations and inputs

    • Statistical analysis of output

    • Continuous and combined models

  • Simulation Software

    • Excel

    • MATLAB/SIMULINK

    • ARENA

ME 4723 – Reliability and Quality Control

  • Reliability concepts

  • Probability and life distribution for reliability analysis 

  • Design for six sigma 

  • Product development

  • Failure modes, mechanisms, and effect analysis 

  • Probabilistic design for reliability and the factor of safety

  • Reliability estimation techniques

  • Process control and process capability

  • Analyzing product failures and root causes

  •  System reliability modeling

  • Warranty analysis

ME 3263 – Manufacturing Engineering

  • Introduction to Manufacturing Engineering: Products, Processes, and Systems

  • Manufacturing Systems: An Overview of Basics

  • Mechanical Properties of Materials

  • Tolerances

  • Measurement and Quality Assurance

  • Manufacturing Processes

EGR 2323 – Applied Engineering Analysis I

  • Mathematical modeling of engineering problems

  • Separable ODE

  •  Integrating factors

  • First-, second-, and higher-order linear constant coefficient ODE’s

  • Non-homogeneous ODE

  • Laplace Transforms

  • s- and t- translation

  • Convolution Solution of an ODE via Laplace transform

  • Existence and uniqueness of solution to a system of linear algebraic equations

  • Gauss elimination and rank

  • Determinant, Cramer’s rule, and inverse of a matrix

  • Eigenvalues and eigenvectors

  • Diagonalization

  • Solutions to system of ODE

 

Teaching at UTSA