Teaching Assistant at Duke

STA 221: Regression Analysis: Theory and Application

Head TA (Fall 2024, Spring 2025)

  • Directed and managed a team of 4 (Fall 2024)/ 5 (Spring 2025) Teaching Assistants to ensure consistent instruction and grading across all course sections for over 35 (Fall 2024)/ 60 (Spring 2025) students.

  • Collaborated directly with the lecturer to design, refine, and edit course materials, including challenging homework and lab problems focusing on regression theory and application.

  • Developed a comprehensive computational review lab to serve as a persistent, reproducible coding reference for all students.

  • Delivered a high-stakes guest lecture on Logistic Regression, demonstrating advanced command of course material.

  • Student Feedback: “Kat is very knowledgeable and knew how to explain questions in a way we could easily understand. She made sure to be patient and encouraged us to understand the process by applying it instead of just memorizing.”


STA 101: Data Analysis and Statistical Inference

Head TA (Spring 2024)

  • Coordinated a TA team of 8 members for a high-enrollment introductory course.

  • Led weekly computational lab sessions and dedicated office hours, translating complex statistical concepts into easily digestible terms.

  • Delivered two guest lectures covering core statistical lessons for the entire student body.

  • Student Feedback: “She knows her stuff. Was really helpful and was always checking in with us. Explained stuff in ways we could understand. Everything seemed common sense when she explained.”


STA 470: Introduction to Statistical Consulting

Fall 2023

  • Mentored and advised students on integrating statistical methods into real-world projects for external clients.

  • Provided critical, constructive feedback focused on the effective communication of complex statistical findings and data visualizations to non-technical audiences.


STA 199: Introduction to Data Science and Statistical Thinking

Fall 2022

  • Provided hands-on assistance to students during labs and office hours, focusing on foundational Data Science principles and R programming.

  • Evaluated student projects and provided detailed guidance on structuring reproducible reporting (using tools like R Markdown/Quarto).

  • Student Feedback: “Kat was great about explaining necessary material that we may not have seen in class while also allowing us to work through problems as a group and not solving problems for us.”