The interactive computing group’s groundbreaking research develops algorithms, designs experiments, and builds systems to answer research questions at different scales: individuals, small groups, and large groups. At the individual level, the group has examined research questions around personal health, supporting work, and, more broadly, the design of just, equitable infrastructures. Our focus on individual health has led to the creation of tools to elicit communication in children and adults diagnosed with Autism Spectrum Disorders (ASD); the creation of environments to support adherence to medical regimens for children with asthma; the development of embodied, mixed-reality rehabilitation systems; the design of algorithmic synthesis of messages; and the development of domestic robots to help aging-in-place. Our research on work environments has resulted in the development of algorithms for opportune notification management, design of decision control and automation systems; the design of visual analytics engines; and the creation of virtual environments for safety. Our emphasis on equity and jus tice has led to the development of contestable machine learning systems; the design of interfaces to communicate algorithmic knowledge and process; the creation of tools that democratize visualization for non-experts; methods to hide on the internet; and the design of adversarial bargaining systems. Our work has led to the creation of the term “algorithmic auditing”, and an increased focus on the ethical implications of the use of algorithms in online platforms. At the small group level, we have examined algorithmic team formation, visualization of group conversations, and public visualizations that incentivize workplace collaboration. At a large scale, our work has examined crowdsourced design critiques and develops educational technologies for remote learning and identifying coordinated behavior. We are exploring novel approaches to online moderation and developing new AI-backed socio-technical systems. Our research pioneered the concept of design mining: using data mining and machine learning to capture and index large repositories of existing designs and correlate the design patterns found in these repositories with performance metrics to understand best practices. We are also building novel data-driven tools for creating and evaluating digital design (developing generative model approaches for predictive design; designing effective user experiences powered by machine learning models; how the design of ranking and rating systems on social platforms can incentivize users to engage more meaningfully with content, to the benefit of everyone).

The group, which comprises senior, mid-career, and junior faculty, including three ACM distinguished members, shows leadership in research, teaching, and service to the community. We publish at leading human-computer interaction and social computing conferences, and the community has recognized our work with best-paper awards and honorable mentions at these conferences. Our scholarship has also been recognized through highly visible external fellowships and with prominent awards within the university. Funding from federal agencies and industry support our work. The group has received acclaim for its teaching and student mentoring. Our faculty have been honored with teaching excellence awards within the university. Our mentoring efforts have led to our undergraduates receiving CRA honorable mentions and our PhD students taking faculty positions at top-tier peer institutions and prominent industry research labs. As part of broader service to the community, we help organize major conferences, associate editors of prominent journals in the field, and as series editors of distinguished academic book collections. Our faculty are leading the Just Infrastructure center that interrogates the complex interactions between people, systems, and algorithms. Our senior faculty are also in leadership positions within the university and have been instrumental in developing the online master’s data science program.

Our public engagement has led to creating public datasets, startups, and to changes in online governance. As part of a collaboration with Google, our faculty have released and maintained the Rico dataset—the largest repository of mobile app designs collected to date—comprising more than 72k unique UI screens and 3M UI elements mined from 9.7k Android apps. This dataset’s scale and its semantic classification of UI components have made it possible for researchers in academia and industry to train deep neural models for mobile task automation, app testing, and UI layout generation. Our work on design mining the Web led to a startup (Apropose, Inc.) that raised significant capital from prominent venture capital funds. We have worked with large-scale Internet platforms, including Twitter, Reddit, and Facebook, to improve online governance. This work led to the ban of hate groups in several online forums and received considerable press coverage—e.g., The New York Times, The Verge, MIT Technology Review, TechCrunch and Motherboard.