I am






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

My research lies at the intersection of robotics, computer vision, and artificial intelligence. My research lab designs intelligent systems that work alongside groups of people in teams in real-world, safety-critical environments (i.e., healthcare). These systems are realized through multi-robot systems, robot vision systems, AI, and augmented and virtual reality devices.

I am the recipient of several awards, including being 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 and Best Paper Award at HRI 2022 and Best Paper and Honorable Mention at CSCW 2019.

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.


[Nov 2021]: Paper accepted to HRI 2022!

[March 2022]: presented REGROUP at HRI 2022!

[march 2022]: won best Paper award at hri!



[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.

*Best Paper Award* 

In Proc. of the 17th Annual ACM/IEEE Conference on Human Robot Interaction (HRI). [Acceptance rate: 24.8%]


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.



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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.




Angelique Taylor, Ph.D.

San Diego, CA 92092

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