I am






I am a Visiting Research Scientist at Facebook Reality Labs Research. I will start my appointment as Assistant Professor at Cornell University in the Summer of 2022!

My research lies in the intersection of computer vision, robotics, healthcare, and artificial intelligence. My work aims to design intelligent systems 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 Ph.D. in Computer Science and Engineering from the University of California San Diego in 2021. Also, I received my B.S. in Electrical Engineering and Computer Engineering from the University of Missouri-Columbia in 2015 and my A.S. 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!

[Feb 2021]: Paper accepted to ICRA!



[Jan 2020]: Paper accepted to THRI!

[Feb 2020]: Paper accepted to AAAI!

[June 2021]: Successfully defended my dissertation!!



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. [Acceptance Rate: 48%]

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.



Screen Shot 2020-12-06 at 10.57.42 PM.pn

Social Navigation for Mobile Robots in the Emergency Department

The emergency department (ED) is a safety-critical environment in which healthcare workers (HCWs) are overburdened, overworked, and have limited resources, especially during the COVID-19 pandemic. One way to address this problem is to explore the use of robots that can support clinical teams, e.g., to deliver materials or restock supplies. However, due to EDs being overcrowded, and the cognitive overload HCWs experience, robots need to understand various levels of patient acuity so they avoid disrupting care delivery. In this paper, we introduce the Safety-Critical Deep Q-Network (SafeDQN) system, a new acuity-aware navigation system for mobile robots. SafeDQN is based on two insights about care in EDs: high-acuity patients tend to have more HCWs in attendance and those HCWs tend to move more quickly. We compared SafeDQN to three classic navigation methods, and show that it generates the safest, quickest path for mobile robots when navigating in a simulated ED environment. We hope this work encourages future exploration of social robots that work in safety-critical, human-centered environments, and ultimately help to improve patient outcomes and save lives. [PDF]




Angelique Taylor, Ph.D.

San Diego, CA 92092

Thanks for submitting!