Photograph of Elaine Schaertl Short

Elaine Schaertl Short

Postdoctoral Fellow
Socially Intelligent Machines (SIM) Lab
Department of Electrical and Computer Engineering
University of Texas at Austin

Research Interests

My work aims to create robots that people want to have around, not because they perfectly imitate human behavior, but because they seamlessly blend into the background while making people's lives easier. These robots will be capable of improving the lives of many people, but will be a life-changing benefit for people with disabilities for whom human assistance comes at a significant cost to privacy and autonomy

Google Scholar Page CV (pdf)

A closeup of the short grey robot Moe using one arm to pick up a block from a table.

Integrating Task and Social Behavior in Time

Social interaction increases the importance of timing, since small changes can have social meaning (e.g., synchronization both results from and reinforces rapport). A core algorithmic challenge I address in my work is how to integrate socially appropriate behavior into temporal models for planning and control algorithms.

A group of people talk to each other and to the small fuzzy robot named Chili.

Efficient Interaction with Groups and Crowds

My work allows robots to influence, understand, and learn from people in groups, while making minimal assumptions about the specifics of their behavior. My goal is to enable robots to appropriately interact with and learn from people in public spaces, with learning algorithms that appropriately integrate information from diverse users and control algorithms that appropriately respond to human behavior in noisy environments.

The small fuzzy robot Chili sits on a table among various food items, playing with a young girl of about five years of age.

Understanding Robot Agency

Agency is the degree to which something or someone can be considered to have free will and autonomy. A robot's appearance of agency depends on both its behavior and the specific person with whom it is interacting. My work in this area contributed to our understanding of how a robot can predict and influence people's perceptions of its agency.


Elaine Schaertl Short is a Postdoctoral Fellow in the Socially Intelligent Machines Lab at the University of Texas at Austin. 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. 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 has been 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.

CV (pdf)


Email is best.

Elaine Schaertl Short
c/o ECE Department
2501 Speedway
EER 6.804, C0806
Austin, TX 78712