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.


[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



[Jan 2020]: Paper accepted to THRI!

[Feb 2020]: Paper accepted to AAAI!

[June 2019]: Recieved thE Microsoft Dissertation Grant!



Taylor, A., matsumoto, s., and Riek, L.D. (2020)

Situating Robots in the Emergency Department.

AAAI Spring Symposium on Applied AI in Healthcare: Safety, Community, and the Environment,  2020.

Taylor, A., Lee, H., Kubota, A., and Riek, L.D. (2019)

Coordinating Clinical Teams: Using Robots to empower nurses to Stop the Line.

*Best Paper Award Honorable mention (top 5% of submissions)* 

 Computer Supported Cooperative Work (CSCW), 2019.

[Acceptance Rate: 30%]

Taylor, A. and Riek, L.D. (2017)

Robot Perception of Social Engagement Using

Group Joint Action.

 In Proc. of the 7th Annual Joint Action Meeting (JAM).

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

Robot-Centric Perception of Human Groups.

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

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

Context Dependent Target Detection

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



RoboCup@Home 2017

As robots enter people's homes, it is important that they are able to effectively accomplish tasks that people perform every day. For the RoboCup@Home 2017 challenge, we designed an algorithm that enabled a Toyota Human Support Robot to transport groceries from a table to a cupboard. Each shelf of the cupboard had a different category of objects (e.g. bottles, can, etc); therefore, we had to ensure that the objects were placed on the correct shelf. We used Simultaneous Localization and Mapping (SLAM) to enable the HSR to autonomously navigate from the table to the cupboard. Additionally, we used a state-of-the-art object detection algorithm, YOLO to detect the different types of objects.




Angelique Taylor

San Diego, CA 92092