Angelique.
Angelique.
I am
Angelique.
TayloR,Ph.d.
ABOUT
ME!
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 Fellow, Google Anita Borg Memorial Scholar, National 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!
RECENT NEWS
2020
2021
2021
2021
2021
2020
2020
[Jan 2020]: Paper accepted to THRI!
[Feb 2020]: Paper accepted to AAAI!
[June 2021]: Successfully defended my dissertation!!
2020
PUBLICATIONS
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., 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.
Chan, D., Taylor, A., and Riek, L.D. (2017)
Faster Robot Perception Using Salient Depth Partitioning
In Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
Taylor, A. and Riek, L.D. (2017)
Robot Affiliation Perception for Social Interaction
In Proc. of Robots in Groups and Teams Workshop at CSCW 2017. pp. 1-4.
Taylor, A., Du, X., Chen, C., Zare, A. (2014)
Context Dependent Target Detection
Computational Intelligence Society Poster Competition, University of Missouri, Columbia.
PROJECTS
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]
ACKNOWLEDGEMENTS
I am thankful to have the support of the National Science Foundation Graduate Research Fellowship (NSF GRFP), Arthur J. Schmitt Presidential Fellowship, GEM National Consortium, Google Anita Borg Memorial Scholarship, National Center for Women in IT (NCWIT), Microsoft Research Dissertation Grant, and the Grace Hopper Celebration of Women in Computing (GHC) Scholarship.
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contact
Angelique Taylor, Ph.D.
San Diego, CA 92092