Teaching

Statistics and education are inseparable. Every methodological development teaches practitioners how to gain understanding from data. Every collaborative project is a venue for teaching proper inference and learning about the intricacies of the subject matter. With the massive role that statistics and data science now play in daily life, the need for formal training is greater than ever.

I approach classroom education with these principles in mind. Whether or not my students become “(bio)statisticians” or “data scientists”, they will be formally and informally analyzing data and hearing statistical results as they pursue their careers and participate in society.

My goal is thus to help students develop a statistical mindset: thinking critically about data and understanding what we can and cannot learn from it. They do this not only through participating in lectures and discussions on statistical concepts and their mathematics, but also through reading scientific studies and data-informed news articles and conducting their own statistical analyses. We cover the value and power of statistics and data, as well as the many ways they have and do go wrong, how they can both improve the world and reinforce existing inequities.

If you’d like to learn more about any of my courses or think that the materials would be useful to you (especially if you are a contingent faculty member, postdoc, or student teaching a course), please reach out to me and I will happily share.

Upcoming Courses (Yale School of Public Health, 2024–)

Biostatistics in Public Health II (BIS 505). This second course in biostatistics introduces public health students in a variety of disciplines to regression-based methods for analyzing public health data. Topics include analysis of variance, linear regression, logistic regression, Poisson regression, survival analysis, and longitudinal regression models. Students develop hands-on R computing skills to perform the analyses discussed. Students also develop their communication and interpretation skills by reporting data analyses to different target audiences and by reading and critiquing published research.
Accelerated Biostatistics (BIS 515). This intensive seven-week summer course for MPH students provides an overview of and introduction to the use of statistics in the fields of epidemiology, public health, and clinical research. Students gain experience conducting and interpreting a broad range of statistical analyses. Topics include descriptive statistics, rules of probability, probability distributions, parameter estimation, hypothesis testing, sample size estimation, nonparametric tests, linear and logistic regression, analysis of variance, and survival analysis. Students develop hands-on R computing skills to perform the analyses discussed. Students also develop their communication and interpretation skills by reporting data analyses to different target audiences and by reading and discussing published research.

Past Courses (Vassar College, 2020–2024)

Probability (MATH 241). In this intermediate-level course, students learn the mathematical basis of the study of probability. This includes the central role calculus plays in defining data-generating distributions and identifying asymptotic results. While the frequentist perspective is emphasized, key Bayesian results are shown and students are encouraged to develop their own understanding that links mathematical axioms of probability to the real world. Example applications include diagnostic testing, stochastic modeling and simulation, infectious disease spread, stock price trends, particle decay, and many others.
Applied Statistical Modeling (MATH 242). Covering linear and logistic regression and methods for handling time-to-event (aka survival or failure time) data, this second course in statistics teaches students the central role of statistical modeling. Examining the assumptions of common methods helps students understand the power and the dangers of statistics. Real-world applications are emphasized, as well as hands-on work with data and the principles of reproducible research.
Clustered and Correlated Data (MATH 301). In this senior seminar, I worked with a group of mathematics and statistics majors to explore methods beyond linear and logistic regression for handling clustered and correlated data. Students learned new methods such as mixed effects models and generalized estimating equations and applied them in projects on topics like energy mix, COVID-19 vaccination rates, and educational outcomes.
Sports, Statistics, and Data (MATH 301). In this senior seminar, we investigate statistical and data science methods used in a variety of sports and games, and how methods developed for sports and related activities have affected statistical development more broadly. Students present methods and examples to one another and work collaboratively to understand and use these methods. The course also explores how statistics and data have affected sports—and its role in and relationship to other aspects of society—through reading responses and course discussion. Finally, students complete a full data analysis project with a sports application.
Statistical Principles for Research Study Design (MATH 348). I designed this course to allow students to explore the role of the statistician in the scientific process. Covering survey studies, randomized experiments, and observational studies, we touch on a variety of study designs, considering both their scientific and statistical advantages and disadvantages. Beyond discussing how statistics can improve study design, we also discuss the key role for ethical considerations and communication and interpretability of study results. Statistics cannot be done in a vacuum.
Quasi-Experiment Statistics (MATH 399). In this independent study, I supervised a small group of students to go beyond their statistics and econometrics coursework and investigate some modern methods of causal inference. Focusing on difference-in-differences and synthetic control, we read the latest research on these methods and implemented them to answer questions about voter ID laws, financial policy, and abortion restrictions.