I am currently a Lecturer in the School of Information at the University of Arizona. I teach courses primarily in the areas of probability, statistics (including Bayesian statistics), and programming in R and Python.

Current Schedule (Fall 2020)

  • ISTA 311: Foundations of Information and Inference
    • ISTA 311 is for students interested in Bayesian inference and methods for inference using limited or noisy data. After a brief review of probability theory and an introduction to the concept of probability as a form of belief, we cover Bayesian inference for categorical and numerical variables, model comparison,
  • ISTA 331: Principles and Practice of Data Science
    • This hands-on introduction to machine learning serves to introduce the core concepts and several example applications to students without requiring extensive mathematical background. Taught in Python using pandas, numpy/scipy, and sklearn, the course emphasizes careful reasoning about machine learning problems as well as the practical skills of data cleaning, model validation and testing, and deployment.
  • ISTA 410 / INFO 510: Bayesian Modeling and Inference
    • This combined-section course, intended for advanced undergraduates and graduate students from a variety of backgrounds. It introduces modern methods in Bayesian modeling, inference by means of simulation, MCMC methods, hierarchical and graphical models, causal inference using DAGs, and several other topics.

Past Courses

  • ISTA 116: Statistical Foundations for the Information Age
    • This introductory statistics course covers data collection principles, summary statistics, data visualization, parameter estimation, hypothesis testing, and regression, and the basics of R and RStudio.
  • ISTA 130: Computational Thinking and Doing
    • This introductory-level course covers the basics of the Python programming language, introduced with no assumption of previous programming or computational experience. ISTA 130 serves as a foundation for more advanced courses in programming and data analysis, such as ISTA 131 and 330.