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Massive datasets are increasingly available for research as digital technologies pervade our lives. These data represent new opportunities for social science research, but prominent examples of data science research bear little resemblance to the research designs of social scientific inquiry. In this course, we use machine learning and statistical tools on large-scale datasets to answer social science questions of cause and effect. Familiarity with Python recommended. Enrollment limited to PhD students in COMM or Social Science who have completed or are currently taking graduate quantitative methods sequences in Economics, Political Science, Sociology, or Statistics. Contact blazzari@stanford.edu for a permission number to enroll (please include a current CV).

This course surveys major theoretical approaches to the study of media technologies, including Frankfurt School critical theory, media archaeology, actor network theory, science and technology studies, platform studies and theories of critical making. By the end of the course, students should have a rich familiarity with the literature in this area, as well as with exemplary empirical studies conducted within each tradition. Preference to Ph.D. students in Communication and Art and Art History. Consent of instructor required for non-PhD students.

The intersection of politics, aesthetics, and new media technologies in the U.S. between the end of WW II and the fall of the Berlin Wall. Topics include the aesthetics of thinking the unthinkable in the wake of the atom bomb; abstract expressionism and 'modern man' discourse; game theory, cybernetics, and new models of art making; the rise of television, intermedia, and the counterculture; and the continuing influence of the early cold war on contemporary media aesthetics. Readings from primary and secondary sources in art history, communication, and critical theory.