Elaine Schaertl Short
Assistant Professor
PI, Assistive Agent Behavior and
Learning (AABL) Lab
Department of Computer
Science
Department of Mechanical
Engineering (secondary appointment)
Tufts University
Assistant Professor
PI, Assistive Agent Behavior and
Learning (AABL) Lab
Department of Computer
Science
Department of Mechanical
Engineering (secondary appointment)
Tufts University
My work lies at the intersection of assistive technology and social robotics (including socially assistive robotics), developing robots that can support people, especially children, older adults, and people with disabilities, in achieving their goals. A key focus of this work is understanding the user as embedded in a social and environmental context, designing new algorithms that allow robots learn from, interact with, and provide assistance to users in real-world environments such as schools, hospitals, and public spaces.
General-purpose robots are unlikely to be deployed with all of the knowledge they need to complete every possible task. We expect that robots will need to learn after deployment, whether to customize their behavior to an individual user, or to learn to handle situations that were not anticipated by the robot designers. In particular, non-expert users in public spaces could be a rich source of information for robots, but the data obtained from these interaction is often noisy and sparse. Our work in this area both enables robots to learn more effectively from non-expert users in noisy real-world environments and equips robots with the social and interaction skills to help non-expert users provide more useful examples.
This work allows robots to quickly and naturally influence, understand, and learn from people in groups and crowds, while making minimal assumptions about the specifics of their behavior. Additionally, we develop fast new algorithms that allow robots to act as lively, responsive, and appealing social partners, while also accomplishing instrumental tasks relating to their embodiment (e.g., driving around a building to show a visitor where to go, or picking up objects to clear them off a table).
Finally, our work is focused on understanding the needs of "non-normative" users, that is people like children, older adults, or people with disabilities, who are not included in the "convenience populations" with which much robotics research is done. Our goal is to use inclusive design and participartory research approaches to gain a better understanding of the needs of these users, and to develop new computational solutions that address real needs at the intersection of physical assistance and social support.
Elaine Schaertl Short is an Assistant Professor of Computer Science at Tufts University. She completed her PhD under the supervision of Prof. Maja Matarić in the Department of Computer Science at the University of Southern California (USC). She received her MS in Computer Science from USC in 2012 and her BS in Computer Science from Yale University in 2010. From 2017-2019 she worked as a postdoctoral researcher in the Socially Intelligent Machines Lab at the University of Texas at Austin. Elaine is a recipient of a National Science Foundation Graduate Research Fellowship, USC Provost’s Fellowship, and a Google Anita Borg Scholarship. At USC, she was recognized for excellence in research, teaching, and service: she was awarded the Viterbi School of Engineering Merit Award and the Women in Science and Engineering (WiSE) Merit Award for Current Doctoral Students, as well as the Best Research Assistant Award, Best Teaching Assistant Award, and Service Award from the Department of Computer Science. At Yale she was the recipient of the Saybrook College Mary Casner Prize. Her research focuses on building algorithms that enable fast, efficient assistive human-robot interaction in schools, homes, crowds and other natural environments.
Email is best.
Elaine Schaertl Short
elaine@eshort.tech
Mailing address:
c/o Department
of Computer Science
177 College Ave
Medford, MA, 02138