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.

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

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 is being 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 has been 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 is also proving to be rewarding.

Graduate Student Instructor for “Spatial Data and Analysis” (PUBPOL 275) with Professor Solomon Hsiang, Fall 2017, University of California, Berkeley (overall effectiveness: 4.5 / 5)

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.

Teaching Assistant for “World Food Economy” (ECON 106) with Professors Rosamond Naylor and Walter Falcon, Winter 2014, Stanford University (overall effectiveness: 4.5 / 5)

This class introduces upper-level undergraduate and graduate students to the economics of food production, consumption, and trade. It overviews the micro- and macro- determinants of food supply and demand, and describes the role of agriculture in poverty alleviation, economic development, and environmental wellbeing. I held office hours and taught the weekly sections of this course, alternating with a co-GSI. In section, I developed and led lectures and group activities to distil, apply, and extend the content from class. A particularly rewarding aspect of the class was mentoring groups of students through their final projects, in which they built simple computable general equilibrium models of the world food economy.

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