Teaching

Every teaching opportunity, from teaching assistant to visiting instructor, has allowed me to dive deeper into the material at hand to better support students.

Graduate Courses:

Visiting Instructor for Applied Biostatistics Methods I

Department of Biostatistics and Bioinformatics, Duke School of Medicine

Instructor: Greg Samsa, PhD

Fall 2024

This course provides an introduction to study design, descriptive statistics, and analysis of statistical models with oneor two predictor variables. Topics include principles of study design, basic study designs, descriptive statistics, sampling, contingency tables, one-and two-way analysis of variance, simple linear regression, analysis of covariance. Both parametric and non-parametric techniques are explored. Computational exercises will use the R and SAS packages.

R Primer Instructor

Department of Population Health Sciences, Duke School of Medicine

Summer 2023

This comprehensive 8-hour lecture was designed to equip incoming Population Health master’s students with a strong foundation in R before their in-person instruction. The session covered key aspects of the R Studio interface, introduced various R data types—including integers, characters, lists, and data frames—and provided an overview of graphics, reporting, and modeling to ensure students were well-prepared for their coursework.

Teaching Assistant for Advanced Introduction to Statistical Theory and Methods II

Department of Biostatistics and Bioinformatics, Duke School of Medicine

Instructor : Roland Matsouaka, PhD

Spring 2023

This course provides formal introduction to the basic theory and methods of probability and statistics. It covers topics in statistical inference, including classical and Bayesian methods, and statistical models for discrete, continuous, and categorical outcomes. Core concepts are mastered through mathematical exploration and simulations. Director of Graduate Studies permission is required.

Teaching Assistant for Introduction to the Practice of Biostatistics I

Department of Biostatistics and Bioinformatics, Duke School of Medicine

Instructor: Jesse Troy, PhD

Fall 2022

This course provides an introduction to biology at a level suitable for practicing biostatisticians and directed practice in techniques of statistical collaboration and communication. With an emphasis on the connection between biomedical content and statistical approach, this course helps unify the statistical concepts and applications learned in BIOSTAT 701 and BIOSTAT 702. Biomedical topics are organized around the fundamental mechanisms of disease from both evolutionary and mechanist perspectives. In addition, students learn how to read and interpret research and clinical research papers. Core concepts and skills are mastered through individual reading and class discussion of selected biomedical papers, team-based case studies, and practical sessions introducing the art of collaborative statistics.

Undergraduate Courses:

Teaching Assistant for Probability Theory

Mathematics Department, Smith College

Instructor: Katherine Halvorsen, PhD

Fall 2020

An introduction to probability, including combinatorial probability, random variables, discrete and continuous distributions.

Teaching Assistant for Introduction to Data Science with R

Statistical and Data Science Department, Smith College

Instructors: Ben Baumer, PhD; Albert Kim, PhD.

Spring 2019, Fall 2019

An introduction to data science using R and SQL. Students learn how to scrape, process and clean data from the web; manipulate data in a variety of formats; contextualize variation in data; construct point and interval estimates using resampling techniques; visualize multidimensional data; design accurate, clear and appropriate data graphics; create data maps and perform basic spatial analysis; and query large relational databases.

Teaching Assistant for Precalculus and Calculus Bootcamp

Mathematics Department, Smith College

Interim 2019

This course provides a fast paced review of and intense practice of computational skills, graphing skills, algebra, trigonometry, elementary functions (precalculus) and computations used in calculus. Featuring a daily review followed by problem solving drills and exercises stressing technique and application, this course provides concentrated practice in the skills needed to succeed in courses that apply elementary functions and calculus. Students gain credit by completing all course assignments, including a final assessment to use in developing their own future math skills study plan.