Lecturer in Engineering and Adjunct Lecturer in Computer Science Ian Gonsher and Assistant Professors Jeff Huang and Stefanie Tellex of Brown University’s Department of Computer Science (Brown CS) have just received Seed Awards from Brown’s Office of the Vice President for Research (OVPR) to help them compete more successfully for large-scale, interdisciplinary, multi-investigator grants. They join numerous previous Brown CS recipients of OVPR Seed Awards, including (most recently) Ugur Cetintemel, Sorin Istrail, Tim Kraska, and Michael Littman.
Ian Gonsher And Stefanie Tellex
One of the interesting aspects of Ian and Stefanie's research is that it sprang from a class project for 1951C Designing Humanity Centered Robots in the fall of 2014. Student input has been ongoing, and they expect it to continue, hopefully inspiring an increasing number of Brown and RISD students to pursue research opportunities. Motivated by some ominous demographic trends (over the next decade, the retiree population is predicted to increase by 33%, while the labor force caring for them is expected to shrink), the researchers propose ubiquitous, minimally invasive, networked robotics as a solution to help older adults age in place.
The stepping stone for the project funded by the OVPR Seed Award is Tablebot, a prototype "situated robot" that's integrated into the built environment and engages the user with movement, telepresence, and artificial intelligence. Building upon this model, the Walkerbot project will create a unique hybrid of telepresence robot and powered walker, designed to be ergonomic but also collect biometric data in real time. This combination will help users not only navigate their environment better but benefit from decreased isolation and loneliness and even allow medical professionals to remotely conduct diagnostics and respond to emergencies.
Jeff's research focuses on the problem of poor sleep, which plagues 60 million Americans. His team will conduct a large-scale study running over 10 years, combining computational and clinical techniques into an automated system that makes actionable recommendations, called SleepCoacher. Their solution uses mobile phones, which are placed on the user's bed and monitor whether recommendations are effective, continuously changing them to slowly improve sleep over an extended period of time.
The SleepCoacher project combines several innovations, from using Bayesian statistical analysis to giving probabilistic interpretations of data to letting users conduct small-scale experiments on their own sleep, helping SleepCoacher learn and adjust its recommendations in a rapid feedback cycle. PhD student Nediyana Daskalova, who has had a core role in this project, noted, "With such distinct differences between individuals' sleep patterns, it takes more than just a couple of nights to help someone improve their sleep, so we need a more refined system to study this issue." The prototype app will soon be on both Android and iOS and the recommendation engine is already compatible with Sleep as Android, a commercial app with more than 10 million installs and 1.5 million active users.
For more information, please click the link that follows to contact Brown CS Communication Outreach Specialist Jesse C. Polhemus.