Group Members

Marynel's research focuses on advancing group Human-Robot Interaction (HRI). She investigates social group phenomena in HRI and develops perception and decision making algorithms to enable autonomous robot behavior. Marynel received her bachelor's degree in Computer Engineering from Universidad Simón Bolívar in 2008, and obtained her M.S. and Ph.D. in Robotics from Carnegie Mellon University in 2013 and 2017, respectively. Before joining Yale, Marynel was a collaborator of Disney Research and a Post-Doctoral Scholar at the Stanford Vision & Learning Lab.

Nathan is a 3rd year PhD student working at the intersection of robotics and machine learning. His research aims to advance the effectiveness and robustness of robotic systems that interact with humans, with the ultimate goal of making robotic platforms more useful in the real world. Prior to Yale, he worked on research at Stanford University in the Stanford Vision & Learning Lab under Silvio Savarese, and worked on machine learning and data engineering at Sequoia.

Sydney is a 3rd year PhD student and is interested in modeling human social behaviors to enable more seamless interactions with robots. Sydney's current projects include studying methods for modeling robot social norm violations in group interactions. She is also working on a robot photographer at Yale to facilitate data collection for group interactions with robots.

Kate is a 2nd year PhD student. She is interested in understanding how we can create robots that are more effectively able to help people. Her current research explores techniques to leverage implicit feedback humans provide naturally during interactions. She is excited about creating robots that can understand and adapt to the preferences of the humans they interact with, creating more positive experiences for users.

Austin is a 1st year PhD student. He earned a B.S. and M.S. in Computer Science from Stanford, where he previously conducted research under the advising of Fei-Fei Li of the Vision Lab and Michael Bernstein of the HCI Group and their joint PhD student Ranjay Krishna. Austin is interested in applying methods in deep learning, computer vision, and natural language processing to enable robots and AI agents to better collaborate with and adapt to unique and diverse users.

Qiping is a 1st year PhD student working on interactive machine learning and human-robot interaction. He is specifically interested in reinforcement learning from human feedback, as well as efficient robot learning of tasks and social rules via intelligent interactions with humans. Prior to joining Yale, he earned a B.S. in Computer Science from the University of Hong Kong, and a M.S. in Computer Science from the University of Texas at Austin, where he conducted research in the Learning Agents Research Group advised by Peter Stone and Scott Niekum.