Interactive Systems Laboratory
Faculty at the University of Michigan Computer Science and Engineering Division who investigate Human Computer Interaction (HCI), Educational Technology, Multimedia, and Social Computing.
Welcome to the Interactive Systems Lab
The Interactive Systems (IS) group at the University of Michigan investigates Human Computer Interaction (HCI), Educational Technology, Multimedia, and Social Computing. HCI is a large and diverse field and the faculty cover many important areas, including strengths in the fundamentals of HCI as well as exciting new technologies and services.
Meet the people who make up the IS Lab >
Prospective graduate students
The scientific fundamentals include the domains of human perception and cognition and human factors, social activity, and learning. The applications cover a wide span: user interface design methods, computational sound and music systems, collaborative systems, access technology, and educational computing in K-12 settings, with a special emphasis on mobile and ubiquitous computing.
Visit our prospective student page on the CSE website >
Towards hybrid intelligence for robotics
Sai Gouravajhala is a PhD student working with Prof. Walter Lasecki on how to create intelligent systems that combine human and machine intelligence to accomplish new tasks.
The UM and Trove collaboration
Profs. Danai Koutra and Walter Lasecki are collaborating with Trove to develop novel methods and tools that will help make intelligent online communication smarter.
There are no events currently scheduled.
Students lead the way on State of Michigan web application to help curb the spread… of COVID-19
“I don’t think any of us expected a global pandemic at the end of our senior year, let alone being able to work on an application that helps address it.”
Web app, dashboard from U-M to inform Michiganders’ return to work
The web tools will help state officials identify potential hotspots as they reopen Michigan to business.
Research on human biases in AI learning earns best student paper award
The project, which received a best paper award, demonstrated that a certain bias in humans who train intelligent agents significantly reduced the effectiveness of the training.