Teaching

I have taught substantive classes as well as methods-oriented classes. I have a great passion for teaching; I view my role as a teacher as a support person for my students. My primary goal as a teacher is to make students engaged and active and to help them learn skills that they will be able to use outside of the contexts of any course and even their field of study. Above all, I strive to make my students better consumers of information. No matter the subject matter, I aim to teach evidence literacy. I believe that active participation is essential to helping students learn, and I structure courses in such a way that there are plenty of ways in which to participate and be active, as I recognize that not all students learn in the same way. I believe strongly that, given all of the challenges today’s students face, being flexible and adaptive is necessary for a good instructor at any level. When I interact with students, I like to remind myself that I am not at the center of their educational experience; I view my work as being in service to them. It is possible to maintain high standards while not placing arbitrary obstacles in students’ ways and thereby throttling their potential. It is important, however, to be open with students about flexibility - silent flexibility favors those who are accustomed to asking for it. Most importantly, I strive to make all students feel included in the classroom. Too many students, particularly from marginalized backgrounds, fall through the cracks of academia because they feel isolated and othered.

For a summary of my teaching evaluations, please click here.

Primary Instructor

University of Massachusetts Amherst

• DACSS 601: Data Science Fundamentals (Fall 2024)
  • Course that introduces DACSS masters students to statistical programming using R
• DACSS 756: Machine Learning for Social Science (Fall 2024)
  • Course for DACSS masters students on machine learning and applying the associated methods to the social sciences

Vassar College (2022 - 2024)

• MATH/CMPU 144: Foundations of Data Science (Fall 2023, Fall 2022)
  • Course that introduces undergraduate students to the basics of data science.
  • Fall 2023 syllabus
  • Fall 2022 syllabus
• MATH 290: Community-Engaged Learning: Political Data Analysis (Fall 2022)
  • Individual learning experience where instructor serves as academic mentor for student working with a community organization
  • Mentee worked with Bluebonnet Data to analyze data for a political campaign in Maryland
• MATH/CMPU 280: Intermediate Data Science (Spring 2023, Spring 2024)
  • Follow up course to MATH/CMPU 144 that focuses on data science tasks like data visualization, web scraping, and machine learning.
  • Spring 2023 syllabus
  • Spring 2024 syllabus
• Liberal Arts Collaborative for Digital Innovation (LACOL) Applied Machine Learning (Summer 2023)
  • Applied machine learning course designed for non-statistics and computer science students.
  • Course page at LACOL

UNC-Chapel Hill (2016 - 2022)

• POLI 281: Data in Politics I: An Introduction (Spring 2021, Fall 2021)
  • Course that introduces undergraduate students to basic data analysis in political science.
  • Fall 2021 syllabus
  • Spring 2021 syllabus
• POLI 784: Regression Models in Political Science Lab (Spring 2019, Spring 2020)
  • Lab section for second course in Ph.D. program’s methods sequence, focusing on introducing the linear model and generalized linear models.
  • Spring 2020 course syllabus
  • Spring 2019 course syllabus
• POLI 130: Introduction to Comparative Politics (Summer 2018, Summer 2019, Fall 2019, Fall 2020)
  • Course that introduces undergraduate students to the basics of comparative politics, with a focus on regime classification and democratization.
  • Fall 2020 syllabus
  • Fall 2019 syllabus