Knowing You Better to do Helpful Things using User Behavior Data
I will present work that leverages user behavioral data to build personalized applications, which I call behavior-powered systems. 1) WebGazer uses interaction data made on any website to continuously calibrate a webcam-based eye tracker, so that users can manipulate any web page solely by looking. 2) SleepCoacher uses smartphone sensors to capture noise and movement data while people sleep to automatically generate recommendations about how to sleep better through a continuous cycle of mini-experiments. 3) Rewind uses passive location tracking on smartphones to recreate a person's past memory through a fusion of geolocation, street side imagery, and weather data. 4) Finally, Drafty tracks interactions with a detailed table of computer science professors to ask the crowd of readers to help keep structured data up-to-date by inferring their interests.
Together, these systems show how subtle footprints of user behavior collected remotely can reimagine the way we gaze at websites, improve our sleep, experience the past, and maintain changing data.
Bio: Jeff Huang is an Assistant Professor in Computer Science at Brown University. His research in human-computer interaction focuses on behavior-powered systems, spanning the domains of mobile devices, personal informatics, and web search. Jeff’s Ph.D. is in Information Science from the University of Washington in Seattle, and his masters and undergraduate degrees are in Computer Science from the University of Illinois at Urbana-Champaign. Before joining Brown, he analyzed search behavior at Microsoft Research, Google, Yahoo, and Bing, and he co-founded World Blender, a Techstars-backed company that made geolocation mobile games. Jeff has been a Facebook Fellow and received a Google Research Award and NSF CAREER Award