Teaching

I teach mathematics and statistics at the University of Arkansas at Little Rock and direct the Math Assistance Center. My teaching emphasizes conceptual understanding, durable public course materials, reproducible work, and the careful use of computational and AI tools.

This portfolio collects ten public course-material sites — two current courses and eight assembled course-material sites — described below.

Current courses

Public, durable resource sites for two of my current courses. Roster- and section-specific material lives in the LMS, not here.

Course identity hero for Intro to Mathematical Software — a blue-violet crystal cluster surrounded by mathematical software, workflow, simulation, and version-control graphics, with the course title.

Intro to Mathematical Software

LaTeX, R, Quarto, reproducible workflow, and careful AI-assisted work.

Course identity hero for Intro to Statistics — an amber crystal cluster surrounded by introductory statistics graphics including distributions, uncertainty, scatterplots, center and spread, and sampling distributions, with the course title.

Intro to Statistics

Data, evidence, models, uncertainty, and simulation.

Each public course site also carries its own notes, labs, and examples alongside curated resource collections — see the Math Software resources and the Intro Stats resources.

Undergraduate course sites

Alongside the two course sites above, I have assembled full public course-material sites for other undergraduate statistics and probability courses. Each is a coherent, self-contained resource site — synthesized notes, hands-on R/Quarto labs, and curated references — built in the same style as the sites above.

These are assembled course materials, not currently scheduled sections. Dates, weights, and final policies are not set here, and anything operational for a live offering would live in the LMS. Treat them as durable, public course resources rather than finished releases.

Course identity hero for Bayesian Statistics — a teal crystal cluster surrounded by Bayesian graphics including Bayes' theorem, a prior-to-posterior update, a posterior predictive band, a 95% credible interval, and an MCMC trace, with the course title.

Bayesian Statistics

Priors, likelihoods, and posteriors; Bayesian regression; simulation, model checking, and hierarchical models.

Course identity hero for Introduction to Probability — an iridescent opal crystal surrounded by probability graphics including sample spaces and events, the probability rules, Bayes' rule, a binomial distribution, the normal curve, expectation, a Monte Carlo simulation sketch, and the law of large numbers, with the course title.

Introduction to Probability

Sample spaces, conditioning and Bayes’ rule, random variables, the standard distributions, and simulation.

Course identity hero for Statistical Inference — a deep-blue crystal cluster surrounded by inference graphics including the inferential problem, a likelihood curve, a confidence interval, a hypothesis test, a sampling distribution, a Bayesian update, the bootstrap, and a randomization test, with the course title.

Statistical Inference

Sampling distributions, likelihood and maximum likelihood, confidence intervals, hypothesis tests, the bootstrap, and Bayesian inference.

Course identity hero for Statistical Modeling — a crimson crystalline cube cluster surrounded by regression graphics including the linear model equation, a residuals-versus-fitted plot, a normal Q-Q plot, a logistic-regression curve, prediction and RMSE panels, and an AIC model-comparison chart, with the course title.

Statistical Modeling

Regression, diagnostics, multiple predictors and adjustment, interactions, prediction and validation, and logistic regression.

Course identity hero for Modern SAS for Statistical Analytics — a banded green malachite crystal surrounded by SAS analytics-workflow graphics including an import-validate-analyze-report-decide pipeline, a DATA step example, a SAS project folder tree, a PROC MEANS table, and a PROC SGPLOT scatterplot, with the course title.

Modern SAS for Statistical Analytics

The professional SAS workflow: DATA steps, PROC SQL, cleaning and validation, the core procedures, ODS output, simulation, and reproducible reporting.

Course identity hero for Design, Experiments and Causal Evidence — a tan desert-rose crystal cluster surrounded by study-design graphics including a design-to-evidence workflow, a causal diagram with a confounder and a backdoor path, and a blocking-improves-precision illustration, with the course title.

Design, Experiments & Causal Evidence

Random sampling vs random assignment, bias and confounding, blocked and factorial experiments, causal diagrams, sampling, and study critique.

Course identity hero for Applied Statistical Methods — a pink-and-green watermelon-tourmaline crystal cluster surrounded by applied-statistics graphics including a linear-model equation, group comparisons and ANOVA, a two-way interaction, regression with diagnostics, a contingency table, and a logistic-regression curve, with the course title.

Applied Statistical Methods

Choosing and connecting methods across groups, factors, covariates, and categorical outcomes: paired and two-group comparisons, one- and two-way ANOVA, regression and ANCOVA, contingency tables, and logistic regression.

Course identity hero for Resampling, Nonparametric and Robust Methods — a red-orange vanadinite crystal cluster surrounded by assumption-light graphics including a bootstrap percentile interval, permutation and randomization tests, rank-based methods, robust estimators with a breakdown-point chart, and a method-comparison simulation, with the course title.

Resampling, Nonparametric & Robust Methods

Assumption-light inference: permutation and randomization tests, the bootstrap, rank-based methods, robust summaries and regression, and simulation studies of how methods behave.

Graduate course sites

Graduate-level course sites are in development in the same style as the collection above. They will be added here as they are assembled.

Course Builder

These ten public sites are the current public demonstration layer for Course Builder, a developing course-design harness I build and use. A Course Builder run does not improvise a course from a prompt; it extends a course as an explicit analytic object under stated controls — course context, source boundaries, pedagogy references, durable state, diagnostic gates, and human review. It is not autonomous course design: instructor judgment and release authority stay central, and graded, answer-key, and roster-specific material stays in the LMS, not on these public sites. See the research page for more.

Teaching approach

  • Concepts before procedures. Students should understand why a method works before reaching for it, so the procedure becomes a tool rather than a ritual.
  • Explain, verify, revise, communicate. I ask students to justify their reasoning, check their results, revise their work, and communicate it clearly — the habits that outlast any single course.
  • Reproducible artifacts matter. Rendered documents, organized projects, executable code, and transparent AI-use notes (when AI is used) make student work durable, checkable, and honest.

Math Assistance Center

As Director of the Math Assistance Center (MAC) at UA Little Rock, I lead the university’s drop-in support center for lower-division mathematics and statistics. The role is part academic service, part leadership:

  • Peer support at scale — coordinating drop-in tutoring that meets students where they are, across many courses and sections.
  • Tutor mentoring and development — hiring, training, and developing peer tutors so they grow as explainers, not just answer-checkers.
  • Instructional coordination — aligning support with what courses actually ask of students, in conversation with the instructors who teach them.
  • Sustainable support workflows — building durable, low-overhead systems so the center keeps running smoothly from term to term.