Welcome to Penn HCI! Located in the University of Pennsylvania’s Computer and Information Science Department, our group studies a range of topics in Human-Computer Interaction with the goal of understanding, designing, engineering, and improving technologies to make a positive impact on individuals and communities.
Penn HCI is founded and led by Assistant Professors Andrew Head and Danaë Metaxa. We are currently recruiting postdocs, graduate students, and undergraduate researchers to continue growing our group.
Danaë spoke with Professor Duncan J. Watts as part of the Annenberg Conversations on Gender Seminar series.
learn moreIn January 2022, the Comp Info Sci department will welcome Andrew Head as an Assistant Professor. Andrew, who will be starting a Penn HCI (Human Computer Interaction) Group with associate new hire Danaë Metaxa, mainly focuses on helping others express their work fluidly and efficiently.
learn moreWhen asked what made them passionate about the work that they do, Danaë Metaxa describes an intrinsic calling to look to the needs of those that scientific design and application neglects.
learn moreView recent publications and filter by topic, author, year, and more.
Princess Sampson, Ro Encarnación, and Danaë Metaxa
FAccT 2023
Targeted online advertising systems increasingly draw scrutiny for the surveillance underpinning their collection of people’s private data, and subsequent automated categorization and inference. The experiences of LGBTQ+ people, whose identities call into question dominant assumptions about who is seen as “normal,” and deserving of privacy, autonomy, and the right to self-determination, are a fruitful site for exploring the impacts of ad targeting. We conducted semi-structured interviews with LGBTQ+ individuals (N=18) to understand their experiences with online advertising, their perceptions of ad targeting, and the interplay of these systems with their queerness and other identities. Our results reflect participants’ overall negative experiences with online ad content—they described it as stereotypical and tokenizing in its lack of diversity and nuance. But their desires for better ad content also clashed with their more fundamental distrust and rejection of the non-consensual and extractive nature of ad targeting. They voiced privacy concerns about continuous data aggregation and behavior tracking, a desire for greater control over their data and attention, and even the right to opt-out entirely. Drawing on scholarship from queer and feminist theory, we explore targeted ads’ homonormativity in their failure to represent multiply-marginalized queer people, the harms of automated inference and categorization to identity formation and self-determination, and the theory of refusal underlying participants’ queer visions for a better online experience.
Litao Yan, Miryung Kim, Björn Hartmann, Tianyi Zhang, and Elena L. Glassman
UIST 2022
Programmers often rely on online resources—such as code examples, documentation, blogs, and Q&A forums—to compare similar libraries and select the one most suitable for their own tasks and contexts. However, this comparison task is often done in an ad-hoc manner, which may result in suboptimal choices. Inspired by Analogical Learning and Variation Theory, we hypothesize that rendering many concept-annotated code examples from different libraries side-by-side can help programmers (1) develop a more comprehensive understanding of the libraries’ similarities and distinctions and (2) make more robust, appropriate library selections. We designed a novel interactive interface, ParaLib, and used it as a technical probe to explore to what extent many side-by-side concept-annotated examples can facilitate the library comparison and selection process. A within-subjects user study with 20 programmers shows that, when using ParaLib, participants made more consistent, suitable library selections and provided more comprehensive summaries of libraries’ similarities and differences.
Litao Yan, Tianyi Zhang, and Elena L. Glassman
CHI 2021
Many programmers want to use deep learning due to its superior accuracy in many challenging domains. Yet our formative study with ten programmers indicated that, when constructing their own deep neural networks (DNNs), they often had a difficult time choosing appropriate model structures and hyperparameter values. This paper presents ExampleNet—a novel interactive visualization system for exploring common and uncommon design choices in a large collection of open-source DNN projects. ExampleNet provides a holistic view of the distribution over model structures and hyperparameter settings in the corpus of DNNs, so users can easily filter the corpus down to projects tackling similar tasks and compare and contrast design choices made by others. We evaluated ExampleNet in a within-subjects study with sixteen participants. Compared with the control condition (i.e., online search), participants using ExampleNet were able to inspect more online examples, make more data-driven design decisions, and make fewer design mistakes.
In this course, you will learn the essentials of human-computer interaction (HCI). Over the course of a semester, you will learn how to design interactive systems that satisfy and delight users by undertaking the human-centered design process, from ideation to prototyping, implementation, and assessment with human users. You will learn key tools in the HCI toolkit, including need-finding, user studies, visual design, cognitive models, demo’ing, ethical considerations, and writing about your designs. This course also provides a primer on several areas of emerging technology in HCI, such as human-AI interaction and education technology. We will also cover ethics in HCI, including topics like inclusive design and dark patterns. To hone your craft as an HCI practitioner, during this course you will undertake a group project to design an innovative user interface. The final submission will include a working interactive prototype, demonstrations of the interface at a public departmental design showcase, and a written reflection on your design findings. Prerequisite: prior programming experience.
Fall 2023
Tuesdays/Thursdays
Danaë Metaxa
In this graduate seminar, we will explore a growing body of work at the intersection of technology and social justice. A range of areas are included under this umbrella including tech ethics, design justice, algorithmic fairness, as well as work on equity, bias, diversity, and representation in computer science and other related disciplines. In this course, students will read and discuss a wide range of this work, through both critical and generative lenses.
Spring 2023
Wednesdays
Danaë Metaxa
In this course, we explore the design of beautiful programs, and tools that can help people construct them. We study tools and guidelines for making programs literate—where code is interleaved with thoughtful explanations—and live—where the behavior of code is exposed. The foundation of the course is a history of live and literate programs, from visionary beginnings to contemporary, beautiful programs and tools for constructing them. We discuss what is known about the design of readable programs based on empirical evidence. We then critique the elements of live and literate programming tools, including: incrementality, reactivity, support for branching, program organization, collaboration, code generation, and the design of programming environments that edit themselves. As case studies, we consider the design of mathematical proofs in proof assistants, computational narratives in notebooks, interactive tutorials, and visionary program editors like Lightable and Eve.
Fall 2022
Mondays
Andrew Head