Bio · Yang is a Senior Research Scientist at Google, and an affiliate faculty member in Computer Science & Engineering at the University of Washington. He earned a Ph.D. degree in Computer Science from the Chinese Academy of Sciences, and conducted postdoctoral research in EECS at the University of California at Berkeley.
Presented a tool that combines programming by demonstration and declaration, via a video-editing metaphor for creating multi-touch interaction.
Proposed and experimented with a new model that extends Fitts' law with a dual-Gaussian distribution for modeling finger touch behaviors.
Investigated various aspects of gesture-based interaction on mobile devices, including gesture-based applications, recognition and tools for creating gesture-based behaviors.
Video CHI Best Paper Honorable Mention AwardPresent a tool that automatically generates code for recognizing each state of multi-touch gestures and invoking corresponding application actions, based on a few gesture examples given by the developer.
Contribute the approaches for bootstrapping a user’s personal gesture library, alleviating the need to define most gestures manually.
Present a tool for random access of smartphone content by drawing touchscreen gestures. It flattens the UI hierarchy of smartphone interfaces.
Investigated attention demands of motion gestures in comparison with traditional interaction techniques for mobile devices.
Present Gesture Avatar, a novel interaction technique that allows users to operate existing arbitrary user interfaces using gestures. It leverages the visibility of graphical user interfaces and the casual interaction of gestures. It outperformed prior techniques especially while users are on the go.
Presents a framework for migrating tasks across devices using mobile cameras. It supports two interaction techniques, Deep Shot and Posting, that enabled direct manipulation of information and work states in a multi-device environment.
Designed a motion gesture for separating intended motion input from ambient motion of mobile phones. A DTW-based recognizer was built to recognize the gesture which had high precision and recall.
Present the results of a guessability study that elicits end-user motion gestures to invoke commands on a smartphone device, which led to the design of a taxonomy for motion gestures and an end-user inspired motion gesture set.
Investigates the impact of situational impairments on touchscreen interaction. Reveals that in the presence of environmental distractions, gestures can offer significant performance gains and reduced attentional load, while performing just as well as soft buttons when the user's attention is fully focused on the phone.
Describes a tool that allows users to access mobile phone data using touch screen gestures. Gesture Search flattens the deep UI hierarchy of mobile user interfaces and learns the mapping from gestures to data items.
Presents a tool for automatically extracting interaction logic from the video recording of paper prototype tests. FrameWire generates interactive prototypes from extracted interaction logic.
Presents an algorithm for recognizing drawn gestures. Protractor employs a closed-form solution to find the best match of an unknown gesture given a set of templates.
Presents the design of a toolkit for gesture-based interaction for touchscreen mobile phones. Introduces the concept of gesture overlays.
Presents a tool that allows designers to incorporate large-scale, long-term human activities as a basis for design, and speeds up ubicomp design by providing integrated support for modeling, prototyping, deployment and in situ testing.
Cascadia is a system that provides RFID-based pervasive computing applications with an infrastructure for specifying, extracting and managing meaningful high-level events from raw RFID data.
Presents the $1 algorithm for gesture recognition and a comprehensive study that evaluates $1 against two other popular gesture recognition algorithms: Dynamic Time Wrapping and Rubine Recognizer. The study indicated that the $1 recognizer though simple outperformed its peers in both accuracy and learnability.
Invited to the SIGGRAPH UIST Reprise SessionPresents the $1 algorithm for gesture recognition and a comprehensive study that evaluates $1 against two other popular gesture recognition algorithms: Dynamic Time Wrapping and Rubine Recognizer. The study indicated that the $1 recognizer though simple outperformed its peers in both accuracy and learnability.
Presents a tool for testing location-based behaviors without specifying interaction logic. The tool explores the extreme of Wizard of Oz approaches for designing field-oriented applications, i.e., testing with zero effort beforehand.
Presents various Wizard of Oz techniques for continuously tracking user locations.
Invited to the SIGGRAPH UIST Reprise Session VideoPresents a tool for creating continuous interactions using examples. Discusses the algorithms for learning continuous interaction behaviors from discrete examples, without using any domain knowledge.
Conducted a study to compare different mode switching techniques for pen-based user interfaces. The study revealed that bi-manual based mode switching outperformed other techniques.
Topiary is a tool for rapidly prototyping location-based applications. It introduces a Wizard of Oz approach for testing location-based applications in the field, without requiring a location infrastructure.
CHI, UIST, TOCHI, IEEE Pervasive Computing, IJHCS, Ubicomp, IUI, ICMI, Pervasive, GI