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My goal as a teacher and mentor is to help students build a strong methodological foundation for creative expression. I seek to convey what we understand in simple and clear terms, and to awaken a desire to study rigorously what we do not.

"Spatial Data Analysis and Remote Sensing” (FRE 490), Term 2 (Jan 8 to Apr 12, 2024), University of British Columbia

This course introduces students to spatial data analysis and remote sensing using the R programming language with a focus on social science applications. The first half of the course teaches students to create, store, manipulate, and analyze spatial data including points, lines, polygons and rasters. The second half of the course introduces students to color- and texture-based approaches for remote sensing, as well as more advanced spatial data analysis methods. Lectures will teach course concepts, sections will review concepts and teach their implementation, and the lab assignments will give students further experience implementing concepts in code. The course has a midterm exam and final project. Note: currently listed as "Current Issues in Food and Resource Economics" (FRE 490 002)

Teaching fellow for “Human Environmental Data Science” (EPS 168) with Professor Peter Huybers, Fall 2020 & 2021, Harvard University

This upper-undergraduate level course introduces students to the foundational scientific principles governing how climate might impact agricultural productivity, social stability, and transmission of infectious disease. By understanding and analyzing these socio-environmental systems, students gain familiarity with simple mathematical models of feedback systems, crop development, and population disease dynamics; frequentist statistical techniques including linear, multiple linear, and panel regression models; and Bayesian methods including empirical, full, and hierarchical approaches. This was the first time this course was taught, and I helped design and implement the structure and content of the course from readings to lectures to coding exercises. A highlight of the course was creating coding exercises that illustrate as sequentially and simply as possible the key techniques and concepts of the course. Mentoring students through independent research projects was also particularly rewarding.


Graduate Student Instructor for “Spatial Data and Analysis” (PUBPOL 275) with Professor Solomon Hsiang, Fall 2017, University of California, Berkeley

This masters-level course teaches students to manipulate, visualize, and analyze spatial data. Through a series of assignments and a final research project, masters and PhD students from a range of disciplines apply skills from spatial statistics, optimization, and remote sensing to questions of public policy, economics and environmental science. I developed and taught the weekly discussion section together with another graduate student instructor. Sections were a mix of interactive lectures (e.g., introducing a concept and then implementing it in code) and small group coding exercises. Responding to feedback, we implemented tools for students to anonymously pose and discuss questions online, as well as a new grading system that shared common mistakes and exemplary insights with all students. The opportunity to advise and mentor students through final applied research projects was particularly rewarding and was a reason I chose to teach this class.

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