Assistant Professor Brian C. Keegan is a computational social scientist whose research is at the intersection of human-computer interaction, network science and data science. He uses computational methods to identify, analyze, and theorize the structural and temporal patterns of large-scale social interaction. This work is motivated the fact that social life rarely unfolds at a steady state; bursts, sequences, and other dynamics play crucial roles in structuring the social world around us. His research uses large datasets from socio-technical information systems such as Wikipedia revision histories, online game user behavior logs, and social media data streams to understand the intersection of temporal dynamics and large-scale social behavior. He is also researching how data science capabilities are being adopted by the emerging cannabis industry.
keywords
Computational social science, Network science, Data science, Computer-mediated communication, New media, Peer production, Crowdsourcing, Computer-supported cooperative work, Web science, Cannabis, Marijuana
INFO 2201 - Computational Reasoning 2: Representations of Data
Primary Instructor
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Spring 2018
Surveys techniques for representing data and expressing relationships among data, both at small scales (for example, via programmatic data structures) and at large scales (for example, in various kinds of database systems). Introduces fundamentals of algorithm analysis and the trade-offs involved in managing data using different approaches, tools and organizing principles. Requires demonstrated proficiency with introductory computer programming.
INFO 3401 - Information Exploration
Primary Instructor
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Fall 2020
Teaches students how to use information to identify interesting real world problems and to generate insight. Students will learn to find, collect, assemble and organize data to inspire new questions, make predictions, generate deliverables, and work towards solutions. They will learn to appropriately apply different methods (including computational, statistical and qualitative) for exploratory data analysis in a variety of domains.
INFO 3402 - Information Exposition
Primary Instructor
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Spring 2018 / Spring 2019 / Fall 2019 / Spring 2021 / Spring 2022 / Fall 2022 / Spring 2023 / Fall 2023 / Spring 2024
Teaches students to communicate information to a wider audience and construct stories with data across a variety of domains. Students will learn to use data for rhetorical purposes, applying visual, statistical and interpretative methods. Students will learn to think critically about ethical and social implications of using data in expository media, including identification of bias.
INFO 3501 - Investigations in Information Science: Open Collaboration
Primary Instructor
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Fall 2018
Analyzes the mechanisms of peer production and crowdsourcing systems like Wikipedia and OpenStreetMap. Students will investigate how these crowdsourced platforms work socially and technically, develop skills using tools for their analysis and critically evaluate platform and community limitations. Counts as Investigations in Information Science. Same as INFO 5501.
INFO 4613 - Network Science
Primary Instructor
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Fall 2022
Introduces theories and methods for analyzing relational data in social, information, and other complex networks. Students will understand the processes and theories explaining network structure and dynamics as well as develop skills analyzing and visualizing real-world network data. No math or statistics training required, but course will assume familiarity with Python. Same as INFO 5613.
INFO 5613 - Network Science
Primary Instructor
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Fall 2021 / Fall 2022
Introduces theories and methods for analyzing relational data in social, information, and other complex networks. Students will understand the processes and theories explaining network structure and dynamics as well as develop skills analyzing and visualizing real-world network data. No math or statistics training required, but course will assume familiarity with Python. Same as INFO 4613.
