Professor Robin Burke conducts research in personalized recommender systems, a field he helped found and develop. His most recent projects explore fairness, accountability and transparency in recommendation through the integration of objectives from diverse stakeholders. Professor Burke is the author of more than 150 peer-reviewed articles in various areas of artificial intelligence including recommender systems, machine learning, and information retrieval. His work has received support from the National Science Foundation, the National Endowment for the Humanities, the Fulbright Commission, and the MacArthur Foundation, among others.
keywords
Recommender systems, Machine learning, Fairness and bias in recommender systems, Multistakeholder recommender systems, Digital humanities, Algorithmic governance
CSCI 4830 - Special Topics in Computer Science
Primary Instructor
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Spring 2021
Covers topics of interest in computer science at the senior undergraduate level. Content varies from semester to semester. Only 9 credit hours from CSCI 4830 and/or CSCI 4831 can count toward Computer Science BS or BA.
CSCI 4950 - Senior Thesis
Primary Instructor
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Fall 2021 / Spring 2022
Provides an opportunity for senior computer science majors to conduct exploratory research in computer science as an option for the capstone requirement. Department enforced prerequisites: 35 hours of Computer Science coursework including Foundation courses, Upper-Division writing, CS GPA 3.0. Department consent required, contact academic advisor for details. May be repeated up to 8 total credit hours.
INFO 3402 - Information Exposition
Secondary Instructor
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Spring 2019
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 4608 - Community-Based Design
Secondary Instructor
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Spring 2019
Surveys techniques in cooperative design with community members as collaborators rather than subjects. Students will explore approaches such as participatory design and co-design. Students will work in teams in partnership with community stakeholders to create tools, experiences, or systems that meet the needs of communities, contribute to social change, and/or lead to advancing academic knowledge. Same as INFO 5608.
INFO 4613 - Network Science
Primary Instructor
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Fall 2021
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 5604 - Applied Machine Learning
Primary Instructor
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Fall 2022
Introduces algorithms and tools for building intelligent computational systems. Methods will be surveyed for classification, regression and clustering in the context of applications such as document filtering and image recognition. Students will learn the theoretical underpinnings of common algorithms (drawing from mathematical disciplines including statistics and optimization) as well as the skills to apply machine learning in practice. Same as INFO 4604.
INFO 5608 - Community-Based Design
Secondary Instructor
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Spring 2019
Surveys techniques in cooperative design with community members as collaborators rather than subjects. Students will explore approaches such as participatory design and co-design. Students will work in teams in partnership with community stakeholders to create tools, experiences, or systems that meet the needs of communities, contribute to social change, and/or lead to advancing academic knowledge. Counts as Mastery in Information Science. Same as INFO 4608.
INFO 5612 - Recommender Systems
Primary Instructor
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Fall 2023
Explores the space of personalized information access applications known as recommender systems. This class will introduce students to a range of approaches for building recommender systems including collaborative, content-based, knowledge-based, and hybrid methods. Students will also explore a variety of applications for recommendation including consumer products, music, social media, and online advertising. The course will also examine controversies surrounding recommendation, including Pariser�s �filter bubble�, and questions of algorithmic bias. Proficiency in Python programming required.
INFO 6500 - Information Science Seminar
Primary Instructor
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Fall 2023 / Spring 2024
Enculturates graduate students in the discipline of Information Science through weekly seminar series that hosts guest speakers, internal faculty and graduate speakers and other community building and professional development activities. May be repeated up to 8 credit hours.
