Teaching

My courses and main teaching interests fall into two broad topics: (1) design and analysis of experiments and observational studies, and (2) statistical communications (writing, presentations, and visualizations). Several of my courses culminate into students creating public-facing statistical objects that I call a part of their "data science portfolio." In this sense, I hope that my students learn not only statistical concepts, but also how to create shareable statistical products that showcase their professional development.


Courses I've taught (ordered by level):


    • Writing in Statistics (36-765), Fall 2020-2021. Train PhD statistics students how to write statistics research papers, as well as how to review and critique peers' drafts.

    • Statistical Practice (36-726), Spring 2021. Advise semester-long Master's capstone projects where students consult with an external client.

    • Undergraduate Research (36-490), Fall 2020-2022, Spring 2020-2023. Advise semester-long undergraduate research projects where students consult with an external client. Students must create public-facing documents at the end of the semester; see here for examples.

    • Introduction to Causal Inference (36-318), Spring 2022-2023. Course I invented, where students learn statistical design and analysis principles for conducting causal inference in experiments and observational studies. In addition to statistical theory and methods, students learn how to implement state-of-the-art regression, matching, weighting, and doubly robust approaches to estimate causal effects.

    • Statistical Graphics and Visualization (36-315), Spring 2020-2021, Fall 2021-2022. Train students to make modern data visualizations that incorporate and/or complement statistical inference. Students must create public-facing data visualizations; see here for examples.

    • Experimental Design for Behavioral and Social Sciences (36-309/36-749), Fall 2019-2022. Train undergraduate and non-statistics graduate students how to design and analyze real experiments (primarily with regression- and ANOVA-based methods). Follows my in-progress textbook with Howard Seltman; see here.