teaching

Please note that all teaching experience described below occurred at the University of Washington iSchool.

Teaching Assistantships

  • INFO371: Advanced Methods in Data Science. Teaching Assistant to Dr. Ott Toomet. Student Rating: 4.9/5 (top 10-20% of TAs during Spring 2023 at the University of Washington). Managed lab sessions, updated programming and data analysis assignments, graded. Course content covers causality, machine learning, introductory Bayesian statistics, and introductory deep learning, including the fundamentals of computer vision and NLP. Spring 2023.
  • INFO370: Core Methods in Data Science. Teaching Assistant to Dr. Ott Toomet. Student Rating: 4.9/5 (top 10-20% of TAs during Winter 2023 at the University of Washington). Managed lab sessions, updated programming and data analysis assignments, graded. Course content covers data analysis, statistical inference, regression, and basic machine learning. Winter 2023.
  • INFO270: Data Reasoning. Teaching Assistant to Dr. Jevin West and Dr. Carl Bergstrom. Student Rating: 4.6/5. Developed and gave two class lectures on AI bias and overhype, created Jupyter notebook used as course material for teaching statistical bias in AI to all discussion sections, participated in panel discussion on scientific uses of AI with course professors, taught discussion sessions, graded assignments. Fall 2022.

Class Lectures and Discussions

  • IMT598: Epistemological Foundations of AI. With Dr. Bill Howe. Gave invited discussion on recent approaches to the use of generative AI in professional fact-checking, contextualizing uses within class materials on AI epistemologies. Spring 2024.
  • INFO466: Moral Reasoning and Interaction Design. With Dr. Alexis Hiniker. Developed lecture and class materials to support students in learning about the ethical consequences of technical design decisions, with particular attention to generative AI. Gave a lecture on pragmatism in design and ran a class workshop. Winter 2023.
  • IMT575: Machine Learning 3: Applications, Scaling, and Ethics. Developed and gave the invited guest lecture on Data Science for Social Good, covering historical trends on applications of AI for good, and recent research on bias in AI. Spring 2022.