I am





I am a PhD Candidate in the Healthcare Robotics Lab in the Computer Science and Engineering department at the University of California San Diego. I work under the direction of Dr. Laurel Riek

My research lies in the intersection of computer vision, robotics, healthcare, and artificial intelligence. My work aims to design algorithms that enable robots to interact and work with groups of people in safety-critical environments. I am also a National Science Foundation GRFP Fellow, Arthur J. Schmitt Presidential Fellow, GEM FellowGoogle Anita Borg Memorial ScholarNational Center for Women in Information Technology (NCWIT), Microsoft Dissertation Grant, and Grace Hopper Celebration of Women in Computing (GHC) Scholar. 

I received my BS in Electrical Engineering and Computer Engineering from the University of Missouri-Columbia in 2015 and my AS in Engineering Science from Saint Louis Community College in 2012.

I am actively pursuing academic faculty positions, postdoc, and industry positions. [Resume]


[March 2020]: Accepted an offer to intern at Facebook ai research

[June 2020]: Passed dissertation proposal!

[April 2019]: Presented my research at the 2019 SoCal Symposium

[Feb 2021]: Paper accepted to ICRA!



[Jan 2020]: Paper accepted to THRI!

[Feb 2020]: Paper accepted to AAAI!

[June 2019]: Recieved thE Microsoft Dissertation Grant!



Taylor, A., Matsumoto, S., Xiao, W., and Riek, L.D. (2021)

Social Navigation for Mobile Robots in the Emergency Department.

In Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2021. To appear.

Taylor, A., Chan, D., and Riek, L.D. (2020)

Robot-Centric Perception of Human Groups.

 ACM Transactions on Human-Robot Interaction (THRI), 2020.

Taylor, A., Du, X., Chen, C., Zare, A. (2014) 

Context Dependent Target Detection

Computational Intelligence Society Poster Competition, University of Missouri, Columbia.




Conceptualizing Robots for the Emergency Department

The emergency department (ED) is a safety-critical environment in which mistakes can be deadly and providers are overburdened. Well-designed and contextualized robots could be an asset in the ED by relieving providers of non-value added tasks and enabling them to spend more time on patient care. To support future work in this application domain, in this paper, we characterize ED staff workflow and patient experience, and identify key considerations for robots in the ED, including safety, physical and behavioral attributes, usability, and training. Then, we discuss the task representation and data needed to situate the robot in the ED, based on this domain knowledge. To the best of our knowledge, this is the first work on robot design for the ED that explicitly takes task acuity into account. This is an exciting area of research and we hope our work inspires further exploration into this problem domain. [PDF]




Angelique Taylor

San Diego, CA 92092