My teaching philosophy is rooted in praxis. I view teaching as a profoundly humanizing act in the Freirein sense, whereby students should not simply acquire knowledge but must be empowered to apply what they learn to relevant real-world contexts that matter. Society benefits when students can harness their critical consciousness to challenge and transform the current social order to create more equitable, just, and inclusive communities. If you are interested in combining solid foundations in data science and programming with your own unique research interests to help solve complex real-world problems, I invite you to join me in the Masters Program in Computational Social Science (MACSS) at the University of Chicago.

Below, you will find brief descriptions of the courses that I currently teach.

Digital Experiments (MACS: 30755)

Spring Quarter

This course takes a hands-on approach to help students develop a deep understanding of the theoretical underpinnings, principles, and methodologies of digital experimentation. Students learn how to design robust and ethical digital experiments in various domains (e.g., online behavior, A/B testing, product design, etc.) while mastering tools and platforms for running digital experiments, such as survey platforms, online experiment frameworks, and analytics tools. Key concepts taught in this course include causal inference, experiment types and validity, factorial designs, sampling, blocking, random assignment, stimuli, mediators, moderators, and effect sizes. Class sessions alternate between lectures & workshops and the culmination of the course requires students to apply their newly acquired knowledge of digital experimental design to solve real-world research problems, e.g., in marketing, behavioral economics, and the social sciences more broadly.

Collective Intelligence (MACS: 30455)

Winter Quarter

This graduate seminar course provides a broad and comprehensive introduction to Collective Intelligence theory and research. Students engage with general science literature drawn from diverse disciplines (e.g., computer science, management science, cognitive science, social psychology, behavioral economics, and political science) to gain a better understanding of collective intelligence and how it emerges (i.e., principles and mechanisms) in diverse contexts, both online and offline. Students learn the theoretical background underpinning the wisdom of crowds, aggregation methods & techniques for pooling individual estimates in collective settings, network dynamics of collective intelligence, and applications of collective intelligence in business, society, and governance. By the end of the course, students synthesize their knowledge, integrate their understanding, and apply it to new areas of research, systems, or experiments.

Perspectives on Computational Research (MACS: 30200)

Spring Quarter

This course focuses on applying computational methods to conducting social scientific research through a student-led research project. Students begin by identifying a research question of their interest that directly references social scientific theory, uses digital data sources, and involves a significant computational component. Then, they collect data and develop, apply, and interpret statistical learning models to generate a fully reproducible research paper. This process helps students identify how computational methods can be used throughout the research process, from data collection and tidying to exploration, modeling, and visualization. This course includes modules on theoretical and practical considerations, including epistemological questions about research design, writing and critiquing papers, and advanced computational tools for analysis.

Perspectives on Computational Analysis (MACS: 30000)

Fall Quarter

This course surveys successful social science applications of computational approaches to the representation of complex data, information visualization, and model construction and estimation. In this course, students examine the scientific method in the social sciences in the context of both theory development and testing, explore how computation and digital data enable new answers to classic investigations, pose novel questions, and discuss new ethical challenges and opportunities for computationally social scientific inquiry. Students also review fundamental research designs such as observational studies and experiments, statistical summaries, visualization of data, and how computational methods can enhance social science research. The main focus of this course is to explore a wide range of contemporary approaches to computational social science.

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“The more students work at storing the deposits entrusted to them, the less they develop the critical consciousness which would result from their intervention in the world as transformers of that world. The more completely they accept the passive role imposed on them, the more they tend simply to adapt to the world as it is and to the fragmented view of reality deposited in them.” – Paolo Freire

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