Developed by the Integrative PhD Fellowship Program:


Visual and Spatial Politics (Fall 2019)

Taught by Victoria Hattam (NSSR  –  Politics) and Jilly Traganou (Parsons – Art and Design History and Theory)


Thinking Through Interfaces (Spring 2019)

Taught by Zed Adams (NSSR – Philosophy) and Shannon Mattern (SPE – Media Studies)

“Thinking Through Interfaces” exposes the hidden lives of interfaces, illuminating not just what they are and how they work, but also how they shape our lives, for better and worse. After examining several case studies and developing a set of analytical frameworks, Zed Adams and Shannon Mattern will invite students to prototype interfaces appropriate to their thesis projects.

Concept Work and Materialization (Fall 2018)

Taught by Janet Roitman (NSSR – Anthropology) and Anthony Dunne (Parsons – Art, Media, and Technology) and Fiona Raby (Parsons – Constructed Environments)

“Concept Work and Materialization” is a project-based seminar that brings together two distinct but complementary modes of inquiry: anthropological inquiry into forms of reasoning and design practice that focuses on experimentation with material forms. The premise of the collaboration is that both modes of inquiry entail “concept work,” or attention to the ways that concepts both shape material practice and can be given form through material practice.\



 Maps as Media

Taught by: Shannon Mattern; NMDS Media Studies 5223

Maps reveal, delineate, verify, orient, navigate, anticipate, historicize, conceal, persuade, and, on occasion, even lie. From the earliest maps in cave paintings and on clay tablets, to the predictive climate visualizations and crime maps and the mobile cartographic apps of today and tomorrow, maps have offered far more than an objective representation of a stable reality. In this hybrid theory-practice studio we will examine the past, present, and future – across myriad geographic and cultural contexts – of our techniques and technologies for mapping space and time. In the process, we will address various critical frameworks for analyzing the rhetorics, poetics, politics, and epistemologies of spatial and temporal maps. Throughout the semester we will also experiment with a variety of critical mapping tools and methods, from techniques of critical cartography and sensory mapping to time-lining, using both analog and digital approaches. Course requirements include: individual map critiques; lab exercises; and individual (or, if desired, small-group) critical-creative atlases composed of maps in a variety of formats, about any topic of the student’s choosing.


Urban Ecologies Methods 3

Taught by: TBA; PGUD Urban Design 5260

The class introduces geospatial technologies as action tools tailored to the analysis and support of participatory projects and community-based research for urban and regional spaces. The class explores open source desktop and web-based geospatial tool-sets for research, design and visualization of urban conditions, as well as strategies for intervention and transformation of issues and challenges facing urban communities. Students will gain valuable experience in harnessing “open databases” and creating innovative mapping tools for the empowerment local urban communities. Open to: MS in Design and Urban Ecologies majors only; others by permission of MS in Design and Urban Ecologies program. Pre-requisite(s): PGUD 5170 Urban Ecologies Methods 2.


Advanced Research in Theories of Urban Practice

Taught by: Gabriela Rendon, Urban Design 5230

Creating Knowledge Through Research Methods. This research course is run as an experimental workshop in which students investigate both conventional and unconventional practices, such as literature searches, fieldwork, quantitative analysis, physical surveys and measurements, historical and archival work, interviews and focus groups, photography and other types of visual documentation, and participatory action research, focusing on the effects of a researcher’s actions on a community. These practices are tested, analyzed, and applied through methods including social networking and remote observation. Each student creates a tool kit tailored to his or her own area of research for the final thesis.


Data Structures

Taught by: Aaron Hill; PGDV Data Visualization 5110

This course covers the fundamentals of the database, semi-structured data, and unstructured data. Students will gain familiarity with data visualization concepts, techniques, and tools, including acquisition, augmentation, and restructuring; data storage and aggregation; access to parallel and distributed computing; high-volume data, disparate sources, and performance; and streaming data and real time and dynamic queries. Open to: Masters degree in Data Visualization majors only; others by permission of Data Visualization program.


Big Data and the Media

Taught by: Robert Berkman; NMDM Media Management 5321

“Big Data” refers to the enormous amount of quantitative, statistical, personal and other unstructured data being generated by people, companies, processes, and even objects. Data analytics can crunch the data in novel ways to uncover patterns and offer fresh insights for making better business decisions. This has critical implications for the media industry. New types of audience data, including social, mobile, and real-time data, are being created, and that is altering the kinds of tools, software, and strategies needed for measuring and making sense of it all.

While Big Data can be a potential boon to researchers, there are cautions. First, how does one separate correlation from causation; pinpoint what truly matters; and take meaningful action? We may also face a temptation to rely on quantitative data and processes when it is not the most useful tool. There are also ethical dimensions in the use of socially generated big data, including scraping of online conversations and assumptions made about customers’ preferences and behaviors.

This course will explore the potential of data and analytics for media marketers and researchers, both through a popular cultural lens (for example, via Nate Silver’s popular book The Signal and the Noise), as well as by focusing on how new types of data streams are changing media market research, including new ethical concerns . We’ll also look at the ability to make predictions from the data (predictive analysis) and how that is already changing the industry. This section is open to Master of Science and graduate certificate Media Management students only. All other graduate students may request access three weeks before the start of the course by emailing


Data, Archives, Infrastructure

Taught by: Shannon Mattern; NMDS Media Studies 5278

There has been more information produced in the last 30 years than during the previous 5000. We’ve all heard some variation on this maxim. As we find ourselves wading through a billion websites; as publishers supply over two million books to the world’s libraries each year; as we continue to add new media from apps to geo-tagged maps to our everyday media repertoires, we continually search for new ways to navigate this ever more treacherous sea of information. Meanwhile, our analog audio-visual archives are deteriorating, and our ever-volatile digital media and data sets present their own preservation challenges. Throughout human history we have relied on various institutions and politico-intellectual architectures to organize, index, preserve, make sense of, and facilitate or control access to our stores of knowledge, our assemblages of media, our collections of information. This seminar looks at the past, present, and future of our archives, libraries, and data repositories, and considers what logics, politics, audiences, contents, aesthetics, physical forms, etc., define them. We will examine what roles these collections play in a variety of contexts: in democracy, in education, in socio-cultural heritage, in everyday life, and in art. Throughout the semester we’ll examine myriad analog and digital artworks that make use of archival/library material, or take the archive, library, or data repository as their subject. Some classes will involve field trips and guest speakers. Students will have the option of completing a substantial traditional research project, or a research-based, theoretically-informed creative/production project for the class.



Visualizing Uncertainty

Taught by: Michael Schober, Aaron Hill; GPSY General Psychology 6422

This seminar brings together data visualization and psychology graduate students to investigate new ways of representing and hypothesizing about data while rigorously questioning what conclusions can legitimately be drawn. How should we think about where the data came from and the methods by which they were generated? What sources of potential measurement error should psychologists and data scientists be concerned about? When can we trust that data collected from nonprobability samples generalize to a full population? When are patterns that emerge in exploratory data visualization trustworthy? How can skepticism and questions about data be communicated with the potential audiences for a visual representation of data? How can we better visualize measurement error and multivariate confidence intervals? Class sessions will combine discussion of academic articles with hands-on examination of existing data sets and practical examples. Psychology and data visualization students will be paired to carry out two hands-on projects during the semester, ideally using their own data from class or thesis projects (although having one’s own data is not required). From these projects, students will gain experience in communicating with collaborators with quite different backgrounds and expertise. Students are only expected to have background knowledge from their own discipline; data visualization students are not expected to have any psychology expertise, and psychology students are not expected to have any coding or design expertise. The course counts as an elective and satisfies the seminar requirement for the Psychology PhD programs; it counts toward any of the umbrella courses for Data Visualization students.


Machine Learning

Taught by: Aaron Hill; PSAM 5020

Machine learning is the systematic study of algorithms and systems that improve their knowledge and performance with experience. Collecting and analyzing data through machine learning algorithms and models can uncover complex patterns in massive amounts to data to make more accurate predictions and to reveal coherent dimensions. The course covers classification, regression, clustering, subgroup, and association models to predict and describe, using supervised and unsupervised learning methods. Special attention will be given to models and techniques that aid and support the visualization of complex data. Open to: All University Graduate students. Some seats have been reserved for MS Data Visualization majors.


Urban Ecologies Methods 2

Taught by: Lize Mogel; PGUD Urban Design 5170

This course familiarizes students with urban research methods that expose and emphasize urban processes, adaptation and change, rather than seeing the city as a static object. Tying ethnographic and co-research methods with representation and mapping skills, this course encourages students to invent tools and methods for representing urban processes. Students workshop group projects as they dialectically emerge from the ethnographic process and explore little-known and often deliberately marginalized processes that affect the production, quality, and use of urban spaces. Open to: MS in Design and Urban Ecologies majors only; others by permission of MS in Design and Urban Ecologies program. Pre-requisite(s): PGUD 5160 Urban Ecologies Methods 1.


Media Research Methods

Taught by: Lauhona Ganguly/ Margaret Bates; NMDS Media Studies 5010

This course focuses on developing specific research methods skills used in media analytics. Students acquire the necessary skills to develop samples for media research and to conduct original research by focusing on two research methods: interviewing and focus groups. Analytics is sometimes seen as purely quantitative, but interviewing and focus groups are invaluable qualitative methods for uncovering user’s media experiences. That knowledge can then help determine what data to analyze. Students will do original research by learning about sampling strategies, then applying those to either interviews or focus groups. Media Studies courses are open to all graduate students. Undergraduate Juniors and Seniors with permission from the instructor and BA/MA Media studies students should email for access.



Participatory Research and Creative Inquiry

Taught by: Nitin Sawhney; NMDS Media Studies 5288

This course explores participatory and qualitative approaches to designing and conducting social inquiry and behavioral research. The class focuses on applying such approaches to understanding the role of participatory media, digital narrative, and DIY cultures in social and community-based contexts, particularly in global settings. Students will examine case studies, theory and practice, as well as ethical considerations for conducting ethnographic fieldwork and qualitative research both online and in place-based communities. We will review approaches to designing qualitative studies, conducting participant observation, focus group sessions and semi-structured interviews, as well as handling informed consent, privacy, and confidentiality. The course also explores novel participatory modes of research leveraging digital media, narrative, mapping and creative expression in diverse socio-cultural settings. Finally, we examine methods for organization and analysis of qualitative data collected in the field to make sense of emerging research outcomes. Students will be expected to conduct brief exercises and devise suitable methods to propose a potential research study of interest. Media Studies courses are open to all graduate students. Undergraduate Juniors and Seniors with permission from the instructor and BA/MA Media studies students should email for access.


Transforming Data: Cultural Strategies in Data Mining

Taught by: Jonathan Thirkield; NMDS Media Studies 5517

From data capture to data visualization, the process of data transformation is a search for meaning through dozens to hundreds to millions of units of information. This course is both a conceptual and practical introduction to contemporary datascape, big and small. The focus will be on the two essential sides of data transformation: making data readable for humans and for machines. Through presentations, readings, and discussions, students will examine a variety of approaches to data research from data structures, to mining and aggregation, to visualization. In pratical projects, students will work with publicly available data sets as well as build their own. Computational methods covered by this class will include APIs, text and image libraries, quantitative methods, maps and GeoCoding. Students will be introduced to programming languages for data management (MySQL and JavaScript), and for visualization (Processing/PSjs). Prior programming experience may be advantageous, but is not necessary.

Pre-Requisite: Media Design (NMDS 5008) or instructor permission. Media Studies courses are open to all graduate students. Undergraduate Juniors and Seniors with permission from the instructor and BA/MA Media studies students should email for access.