Spring 2019 Courses

Roshanna Sylvester
Lecture: TuTh 8am-9:15am, Duane G130 / TuTh 12:30pm-1:45pm, Muenzinger E0046

Introduces students to modern information and communication technology, the basic principles of software and programming, the fundamental role of algorithms in modern society, computational reasoning, the major organizations in the information sector and fundamental interactions between humans and information technology. Appropriate for students with limited prior experience with computing.

Dr. Laura Devendorf
Lecture: TuTh, 12:30-1:45PM, Clare 207

Provides an introduction to human-centered design and the universal requirements of interactions with data, information and technologies. Studio experiences challenge students to consider the impact that information and computing technology design choices have on a) enabling diverse audiences to access, manipulate and experience information, and b) how differences get encoded by data and technology, ultimately reflecting biases.

Jason Zietz
Lecture: MWF 12:00PM-12:50PM, Muenzinger E050

Introduces principles of computational thinking through the manipulation, transformation and creation of media artifacts, such as images, animations, sounds, web pages, data visualizations and games. Students will be exposed to a high-level overview of how algorithms, functions and data structures are used in computer programming through a series of assignments that emphasize the use of computation as a means of creative expression.

Jason Zietz
Lecture: MWF 9:00AM-9:50AM, Eaton Humanities 1B80

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.

Dr. Michael Paul
Lecture: MWF 10:00AM-10:50AM, Muenzinger E113

Introduces methods for quantifying and analyzing different types of data, covering foundational concepts in discrete mathematics, probability, and predictive modeling, along with complementary computational skills to apply these concepts to real problems. Covers counting and combinatorics, logic, set theory, introductory probability, common probability distributions, regression, and model validation. Requires demonstrated proficiency with introductory computer programming.

Dr. Brian Keegan
Lecture: MWF 11:00AM-11:50AM, Center for Academic Success and Engagement E250

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.

Dr. Ricarose Roque
Seminar: W 4:00PM-6:30PM, Ketchum 1B71

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.

Dr. Stephen Voida
Lecture: TuTH 11:00AM-12:15PM, Center for Academic Success and Engagement E250

Introduces the field of ubiquitous computing, including sensors, ambient displays, tangibles, mobility, location awareness and context awareness. These topics are explored from a user-centered design perspectives, focusing on how a situated models of computing affect requirements gathering, interaction design, prototyping and evaluation. Students gain mastery with contemporary "UbiComp" technologies and learn to incorporate them into a user-centered design process.

Jason Zietz
Seminar: M 3:00PM-5:30PM, TBD

Provides senior level INFO students an opportunity to demonstrate the culmination of their learning in the major by designing and implementing a significant information system or developing a research question, typically in response to a problem of personal interest related to or informed by their secondary area of specialization. Reinforces project planning, public presentation and ethic skills.

Dr. Robin Burke
Lecture: TuTh 2:00PM-3:15PM, Center for Academic Success and Engagement E250 / TuTh 3:30PM-4:45PM, Ketchum 1B71

This is a research seminar that will explore 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”, the deployment of personalization as a tool for electoral manipulation, and questions of algorithmic bias.

Dr. Jed Brubaker
Seminar: F 2:30PM-5:00PM, TBD

Surveys foundational theories and concepts in information science. Students will learn to read and reflect critically about seminal texts, tracing their intellectual genealogies from a variety of originating disciplines to their appropriation by information science. Students will apply these theories to contemporary issues and problems.

Dr. Amy Voida
Seminar: W 2:00PM-3:00PM, TBD

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.