I am

Angelique.

Taylor

ABOUT

ME!

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!

RECENT NEWS

2020
2021
2019
 
2019
2019
2020
2020

[Jan 2020]: Paper accepted to THRI!

[Feb 2020]: Paper accepted to AAAI!

[June 2019]: Recieved thE Microsoft Dissertation Grant!

2020

PUBLICATIONS

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.

 
 

PROJECTS

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]

PRESS
 RoboCup@Home2017 
 
 

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contact

Angelique Taylor

San Diego, CA 92092

©2019