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 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 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!
[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!
Situating Robots in the Emergency Department.
AAAI Spring Symposium on Applied AI in Healthcare: Safety, Community, and the Environment, 2020.
Coordinating Clinical Teams: Using Robots to empower nurses to Stop the Line.
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).
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%]
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.
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]
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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San Diego, CA 92092