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.
My research lies at the intersection of robotics, computer vision, and artificial intelligence. My research lab designs intelligent systems to support people in their everyday lives, and in safety-critical environments (i.e., healthcare) to improve human performance using robots, augmented and virtual reality devices, and human-computer interaction interfaces.
I design perception and decision-making systems to enable robots to work in teams with groups of people in real-world, safety-critical environments. I'm interested in solving problems in multi-robot multi-human coordination particularly within the context of healthcare. I am interested in addressing perception, decision-making, and machine learning techniques to enable intelligent systems to address real-world problems.
I'm also interested in augmented reality and virtual reality technologies to create immersive experiences that help increase access to health services (e.g., telemedicine, mental health) and to help improve human performance particularly during collaborative team tasks.
I am the recipient of several awards, including being 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 am excited to join the faculty of Cornell Tech in New York City (NYU) next summer! I am currently recruiting students to join my lab. If you are interested in working with me, be sure to mention me in your statement of purpose. Given that our interests align, I will contact you to learn more!
[Nov 2021]: Paper accepted to HRI 2022!
[June 2020]: Passed dissertation proposal!
[Feb 2021]: Paper accepted to ICRA!
[Oct 2021]: Presented SafeDQN aT Facebook AI Organization!
[Nov 2021]: Accepted Job Offer at Cornell Tech!!
[June 2021]: Successfully defended my dissertation!!
Taylor, A. and Riek, L.D. (2022)
REGROUP: A Robot-Centric Group Detection and Tracking System.
In Proc. of the 17th Annual ACM/IEEE Conference on Human Robot Interaction (HRI). [Acceptance rate: 24.8%]
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.
REGROUP: A Robot-Centric Group Detection and Tracking System
To facilitate HRI's transition from dyadic to group interaction, new methods are needed for robots to sense and understand team behavior. We introduce the Robot-Centric Group Detection and Tracking System (REGROUP), a new method that enables robots to detect and track groups of people from an ego-centric perspective using a crowd-aware, tracking-by-detection approach. Our system employs a novel technique that leverages person re-identification deep learning features to address the group data association problem. REGROUP is robust to real-world vision challenges such as occlusion, camera egomotion, shadow, and varying lighting illuminations. Also, it runs in real-time on real-world data. We show that REGROUP outperformed three group detection methods by up to 40% in terms of precision and up to 18% in terms of recall. Also, we show that REGROUP's group tracking method outperformed three state-of-the-art methods by up to 66% in terms of tracking accuracy and 20% in terms of tracking precision. We plan to publicly release our system to support HRI teaming research and development. We hope this work will enable the development of robots that can more effectively locate and perceive their teammates, particularly in uncertain, unstructured environments.
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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Angelique Taylor, Ph.D.
San Diego, CA 92092