Research
Designing technology that is more intuitive, accessible, and useful across the full range of human experiences.
People with visual impairments are expert users of audio interfaces, including voice-activated virtual assistants and screen readers. Through interviews and surveys of this population, we learned that virtual assistants are convenient and accessible, but lack…
Emerging technologies like artificial intelligence and mixed reality have the potential to upend the way we create, work, and interact with one another. This disruption is an opportunity and a challenge. As we see technologies…
We have all heard the saying that a picture is worth a thousand words. It is particularly true in STEM fields where technical diagrams are used universally as a core construct for communicating ideas. And…
Charticulator is an interactive authoring tool that enables the creation of bespoke and reusable chart layouts. Charticulator is our response to most existing chart construction interfaces that require authors to choose from predefined chart layouts,…
Tasks—defined pieces of work ranging in scope from the specific, such as sending an email, to the broad, such as shipping a feature—are central to all aspects of personal and team productivity. Task intelligence spans…
Artificial intelligence has the potential to improve productivity throughout the workplace by leveraging how people communicate to proactively connect a person to the right people and information. Providing this benefit requires key system capabilities, including…
In today’s world, people have to attend to a number of tasks near simultaneously, and with the widespread use of mobile devices, tasks can be tackled almost anywhere at any time. It is not surprising,…
The bistring library provides non-destructive versions of common string processing operations like normalization, case folding, and find/replace. Each bistring remembers the original string, and how its substrings map to substrings of the modified version.
The project is an official implementation of our CVPR2019 paper “Deep High-Resolution Representation Learning for Human Pose Estimation”
