My primary interests are in applied statistics and the philosophy of statistics. Most of my work has been in statistical methods for photovoltaic (solar cell) performance modeling. I have also worked on applications for residential building energy analysis and on a consulting team that provides statistical litigation support. In addition to math and statistics, I am also interested in a number of areas in philosophy, including ethics, philosophy of science, and phenomenology.
APPM 3310  Matrix Methods and Applications
Primary Instructor

Spring 2018
Introduces linear algebra and matrices with an emphasis on applications, including methods to solve systems of linear algebraic and linear ordinary differential equations. Discusses vector space concepts, decomposition theorems, and eigenvalue problems. Degree credit not granted for this course and MATH 2130 and MATH 2135.
APPM 4570  Statistical Methods
Primary Instructor

Spring 2018
Covers basic statistical concepts with accompanying introduction to the R programming language. Topics include discrete and continuous probability laws, random variables, expectation and variance, central limit theorem, testing hypothesis and confidence intervals, linear regression analysis, simulations for validation of statistical methods and applications of methods in R. Same as APPM 5570.
APPM 5570  Statistical Methods
Primary Instructor

Spring 2018
Covers basic statistical concepts with accompanying introduction to the R programming language. Topics include discrete and continuous probability laws, random variables, expectation and variance, central limit theorem, testing hypothesis and confidence intervals, linear regression analysis, simulations for validation of statistical methods and applications of methods in R. Same as APPM 4570.
DTSA 5011  Modern Regression Analysis in R
Primary Instructor

Summer 2021 / Fall 2021 / Spring 2022 / Summer 2022 / Fall 2022 / Spring 2023 / Summer 2023 / Fall 2023 / Spring 2024 / Summer 2024 / Fall 2024
Modern Regression Analysis in R provides foundational statistical modeling tools for data science. Introduction to methods, theory, and applications of linear statistical models, covering the topics of parameter estimation, residual diagnostics, goodness of fit, and various strategies for variable selection and model comparison. Attention will also be given to the misuse of statistical models and ethical implications of such misuse.
DTSA 5012  ANOVA and Experimental Design
Primary Instructor

Summer 2021 / Fall 2021 / Spring 2022 / Summer 2022 / Fall 2022 / Spring 2023 / Summer 2023 / Fall 2023 / Spring 2024 / Summer 2024 / Fall 2024
Introduction to the analysis of variance (ANOVA), analysis of covariance (ANCOVA), and experimental design. ANOVA and ANCOVA, presented as a type of linear regression model, provide mathematical basis for designing experiments for data science applications. Emphasis placed on important designrelated concepts, such as randomization, blocking, factorial design, and causality. Attention will also be given to ethical issues raised in experimentation.
DTSA 5013  Generalized Linear Models and Nonparametric Regression
Primary Instructor

Fall 2021 / Spring 2022 / Summer 2022 / Fall 2022 / Spring 2023 / Summer 2023 / Fall 2023 / Spring 2024 / Summer 2024 / Fall 2024
Generalized Linear Models and Nonparametric Regression teaches generalized linear models (GLMs), which provide an introduction to classification (through logistic regression); nonparametric modeling, including kernel estimators, smoothing splines; and semiparametric generalized additive models (GAMs). Emphasis will be placed on a firm conceptual understanding of these tools. Attention will also be given to ethical issues raised by using complicated statistical models.
MATH 4520  Introduction to Mathematical Statistics
Primary Instructor

Fall 2018 / Fall 2019 / Fall 2020 / Fall 2022
Examines point and confidence interval estimation. Principles of maximum likelihood, sufficiency, and completeness: tests of simple and composite hypotheses, linear models, and multiple regression analysis if time permits. Analyzes various distributionfree methods. Same as MATH 5520 and STAT 4520 and STAT 5520.
MATH 5520  Introduction to Mathematical Statistics
Primary Instructor

Fall 2019
Examines point and confidence interval estimation. Principles of maximum likelihood, sufficiency, and completeness: tests of simple and composite hypotheses, linear models, and multiple regression analysis if time permits. Analyzes various distributionfree methods. Department enforced prerequisite: one semester calculusbased probability course, such as MATH 4510 or APPM 3570. Recommended prerequisite: previous coursework equivalent to APPM 3570 or STAT 3100 or MATH 4510; minimum grade of C for all. Same as STAT 4520 and MATH 4520 and STAT 5520.
STAT 3400  Applied Regression
Primary Instructor

Spring 2019
Introduces methods, theory, and applications of linear statistical models, covering topics such as estimation, residual diagnostics, goodness of fit, transformations, and various strategies for variable selection and model comparison. Examples will be demonstrated using statistical programming language R.
STAT 4010  Statistical Methods and Applications II
Primary Instructor

Spring 2019 / Spring 2020 / Spring 2021 / Spring 2022 / Spring 2023
Expands upon statistical techniques introduced in STAT 4000. Topics include modern regression analysis, analysis of variance (ANOVA), experimental design, nonparametric methods, and an introduction to Bayesian data analysis. Considerable emphasis on application in the R programming language. Same as STAT 5010.
STAT 4520  Introduction to Mathematical Statistics
Primary Instructor

Fall 2018 / Fall 2019 / Fall 2020 / Fall 2022
Examines point and confidence interval estimation. Principles of maximum likelihood, sufficiency, and completeness: tests of simple and composite hypotheses, linear models, and multiple regression analysis if time permits. Analyzes various distributionfree methods. Same as STAT 5520 and MATH 4520 and MATH 5520.
STAT 4630  Computational Bayesian Statistics
Primary Instructor

Spring 2022 / Spring 2023 / Spring 2024
Introduces Bayesian statistics, normal and nonnormal approximation to likelihood and posteriors, the EM algorithm, data augmentation, and Markov Chain Monte Carlo (MCMC) methods. Additionally, introduces more advanced MCMC algorithms and requires significant statistical computing. Examples from a variety of areas, including biostatistics, environmental sciences, and engineering, will be given throughout the course. Recommended prerequisite: prior programming experience. Same as STAT 5630.
STAT 4700  Philosophical and Ethical Issues in Statistics
Primary Instructor

Fall 2019 / Fall 2021 / Fall 2022 / Fall 2023 / Fall 2024
Introduces students to philosophical issues that arise in statistical theory and practice. Topics include interpretations of probability, philosophical paradigms in statistics, inductive inference, causality, reproducible, and ethical issues arising in statistics and data analysis. Same as STAT 5700.
STAT 5000  Statistical Methods and Application I
Primary Instructor

Fall 2021
Introduces exploratory data analysis, probability theory, statistical inference, and data modeling. Topics include discrete and continuous probability distributions, expectation, laws of large numbers, central limit theorem, statistical parameter estimation, hypothesis testing, and regression analysis. Considerable emphasis on applications in the R programming language. Recommended prerequisites of APPM 1360 or MATH 2300 or equivalent. Same as STAT 4000.
STAT 5010  Statistical Methods and Applications II
Primary Instructor

Spring 2019 / Spring 2020 / Spring 2021 / Spring 2022 / Spring 2023
Expands upon statistical techniques introduced in STAT 4000. Topics include modern regression analysis, analysis of variance (ANOVA), experimental design, nonparametric methods, and an introduction to Bayesian data analysis. Considerable emphasis on application in the R programming language. Same as STAT 4010.
STAT 5520  Introduction to Mathematical Statistics
Primary Instructor

Fall 2018 / Fall 2019 / Fall 2020 / Fall 2022
Examines point and confidence interval estimation. Principles of maximum likelihood, sufficiency, and completeness: tests of simple and composite hypotheses, linear models, and multiple regression analysis if time permits. Analyzes various distributionfree methods. Department enforced prerequisite: one semester calculusbased probability course, such as MATH 4510 or APPM 3570. Recommended prerequisite: previous coursework equivalent to APPM 3570 or STAT 3100 or MATH 4510; minimum grade of C for all. Same as STAT 4520 and MATH 4520 and MATH 5520.
STAT 5630  Computational Bayesian Statistics
Primary Instructor

Spring 2022 / Spring 2023 / Spring 2024
Introduces Bayesian statistics, normal and nonnormal approximation to likelihood and posteriors, the EM algorithm, data augmentation, and Markov Chain Monte Carlo (MCMC) methods. Additionally, introduces more advanced MCMC algorithms and requires significant statistical computing. Examples from a variety of areas, including biostatistics, environmental sciences, and engineering, will be given throughout the course. Recommended prerequisite: prior programming and basic statistical modeling experience is required. Same as STAT 4630.
STAT 5700  Philosophical and Ethical Issues in Statistics
Primary Instructor

Fall 2019 / Fall 2021 / Fall 2022 / Fall 2023 / Fall 2024
Introduces students to philosophical issues that arise in statistical theory and practice. Topics include interpretations of probability, philosophical paradigms in statistics, inductive inference, causality, reproducible, and ethical issues arising in statistics and data analysis. Recommended prerequisite: previous coursework equivalent to STAT 3400 or STAT 4000 or STAT 4520 or STAT 5000 or STAT 5520 or STAT 5530; minimum grade C for all. Same as STAT 4700.