Tyler Simko

University of Michigan

• POLSCI 797: Complex Data and Machine Learning for Political Scientists (Graduate)
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  Political scientists are increasingly using non-traditional sources of data containing spatial, text, image, audio, and video information. Further, modern machine learning techniques enable social scientists to investigate new kinds of research questions aimed at descriptive, predictive, and causal inference. The first goal of this course is to introduce students to these non-traditional kinds of data and to build the necessary research computing skills to analyze them. The second goal of this course is to introduce modern machine learning techniques. Throughout, we will cover core skills in a computational social scientist’s toolkit like version control, data visualization, web scraping, and presentation skills for technical content.
• POLSCI 489: Urban and Local Politics (Undergraduate)
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  Local governments play key roles in policy areas like housing, policing, education, environmental sustainability, public health, and transportation. This course will examine the politics of these subnational governments with a particular focus on three themes: space (e.g. political geography, land use, and spatial inequality), race (e.g. group conflict, power, and race/ethnicity), and place (e.g. neighborhood effects, place-based identities, community organizing and mobilization). Though readings will emphasize the American case, we will consider local governments across many federal systems and draw from recent work in comparative politics, sociology, economics, and public policy.

In 2023, I was awarded Harvard's university-wide Derek C. Bok Award for Excellence in Graduate Student Teaching of Undergraduates. In graduate school, I designed my own course on US Local Policymaking in Spring 2022 for Harvard undergraduates, re-designed the summer data science training for MPA/ID students at the Harvard Kennedy School in 2022, and created an Introduction to Data Science (in R) in Summer 2021 for high school students in New Jersey. During graduate school, I also served as a Teaching Fellow for several courses on statistics, American politics, and computational social science.

Other Teaching Materials

• R Package: Imai's Quantitative Social Science
• I co-authored the R package used in Kosuke Imai's textbook on quantitative social science.
• GIS Teaching Materials: Spatial Models of Social Science
• Harvard University, Fall 2020 (Instructor: Jill Kelly).
• I helped to create the teaching materials for a course on airborne and satellite remote sensing (primarily with ArcGIS).
• Gradescope Autograder
• Gradescope is a useful tool for automatically grading certain assignments when teaching. The full autograder setup for R assignments that I have used in the past is available here.