Teaching

Classes at North Carolina State University

• ST 442: Introduction to Data Sciences. Data Science has become increasingly important in nearly every industry section and academic field. It has gained significant national attention and interest by combining techniques from several fields like Computer Science, Statistics, and mathematics to extract knowledge from data. This course provides an overview of several foundational topics in Data Science and will expose students to the theory and algorithms underlying these techniques, as well as the use of common commercial packages. The class will include a mix of lectures and programming projects.
• ST 515: Experimental Statistics for Engineers I. General statistical concepts and techniques useful to research workers in engineering, textiles, wood technology, etc. Probability distributions, measurement of precision, simple and multiple regression, tests of significance, analysis of variance,enumeration data and experimental design.
• ST 740: Bayesian Inference and Analysis. Introduction to Bayesian inference; specifying prior distributions; conjugate priors, summarizing posterior information, predictive distributions, hierachical models, asymptotic consistency and asymptotic normality. Markov Chain Monte Carlo (MCMC) methods and the use of exising software.

Classes at George Washington University

• ST 6289.16: Bayesian Computation. Applied Bayesian methods with special emphasis on Bayesian computational tools. Bayesian estimation, intervals, inference, hypothesis testing, linear and generalized linear models.

Classes at Iowa State University

• STAT 101: Principles of Statistics. Statistical concepts in modern society; descriptive statistics and graphical displays of data; the normal distribution; data collection (sampling and designing experiments); elementary probability; elements of statistical inference; estimation and hypothesis testing; linear regression and correlation; contingency tables.
• LAS 290G: Special Problems/Understanding Technical Risk: Culture and Analysis. Freshman seminar exploring social and technical aspects of risk through a series of case studies including bioterrorism, HIV/AIDS, and nuclear nonproliferation.
• STAT 326: Introduction to Business Statistics II. Multiple regression analysis; regression diagnostics; model building; applications in analysis of variance and time series; random variables; distributions; conditional probability; statistical process control methods; use of computers to visualize and analyze data.
• STAT 544: Bayesian Statistics. Specification of probability models; subjective, conjugate, and noninformative prior distributions; hierarchical models; analytical and computational techniques for obtaining posterior distributions; model checking, model selection, diagnostics; comparison of Bayesian and traditional methods.