Postdoctoral Researchers
ude.csu@gihs Website
Guangyao mainly works on coordination and learning algorithms for multi-robot decision-making under uncertainty with an emphasis on information gathering applications.
PhD Students
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Sumeet is interested in biologically plausible learning algorithms and models and their applications to robotics. Previously he worked on Quality Diversity Reinforcement Learning (QD-RL) and generative models for control. He is currently working on QD + language for open ended skill discovery, and planning as inference.
ude.csu@rraduihc Website
Darren is generally interested in multi robot systems that combine novel hardware design with unique algorithmic approaches. Currently, his research focuses on event based visual navigation through a swam of resource constrained quadrotors. Before coming to USC, Darren received my B.S.E in Electrical and Computer Engineering from Princeton University.
ude.csu@eejaytas Website
Satyajeet’s research interests lie at the intersection of machine learning, robotics, and computer vision, focusing on making learning algorithms more explainable, generalizable, and efficient for robotics. He envisions a future where humans and robots collaborate seamlessly, ensuring safety, mutual growth, and harmonious coexistence. Before joining USC, he earned a master’s degree from the University of Pennsylvania.
ude.csu@egiahour Website
Ruohai’s research interests lie in multi-robot systems, active navigation, and field robotics. Before joining USC, he earned a Master’s degree in Computer Science and a Bachelor’s degree in Electrical and Computer Engineering from Carnegie Mellon University.
ude.csu@edgehk Website
Shashank’s research interests include deep reinforcement learning, machine learning, and robotics. Shashank is a strong believer in the power of AI to improve the quality of life of people around the world.
ude.csu@uhiuhehz Website
Zhehui’s goal is to build intelligent agents that with the ability to perform complex tasks robustly and safely within unstructured environments via autonomous and unsupervised adaptation to the environment. He focuses on reinforcement learning, robot learning and foundation models.
ude.csu@ikenujoh Website
Hojune is interested in studying interactions within decentralized multi-robot systems. Specifically, his research focuses on integrating mathematical tools—such as game theory and probabilistic inference—with machine learning methods to develop robust systems that can operate under various forms of uncertainty. Before joining USC, Hojune graduated from the Honors Program at Swarthmore College with a B.A. in Mathematics and a B.S. in Engineering.
ude.csu@nauhsnuy Website
Yunshuang received a M.S from UPenn and a B.E. in both automatic control and Mechatronic Engineering from Chu Kochen Honors College, Zhejiang University. Her research interests are in robotics, machine learning, and computer vision, especially the applications of CV in robot perception.
ude.csu@uilnuhci Website
Arthur’s research interests lie in the fields of imitation learning and reinforcement learning for robotic manipulation. Currently, his research focuses on vision-based bimanual manipulation. Prior to joining USC, he completed a master’s degree in Computer Science from the University of California, Los Angeles (UCLA), and a bachelor’s degree in Computer Science from the University of Wisconsin-Madison.
ude.csu@ejnagrom Website
Jeremy’s research interests include developing novel Inverse Kinematics solvers and planning algorithms for manipulators. In the future he will transition to look at dexterous manipulation problems.
ude.csu@teijgnib Website
Bingjie is interested in learning-based approach for robot manipulation and simulation-to-reality transfer for contact-rich manipulation tasks. Before joining USC, Bingjie received an MS in Computer Science from Brown University and a bachelor degree in Computer Science from Huazhong University of Science and Technology.
ude.csu@21uy.iynix Website
Xinyi’s research interests center on robotics, and she is broadly interested in leveraging diverse technologies, including but not limited to learning and optimization. Recently, she focuses on robotic manipulators to ground algorithmic innovations in physical autonomy. Furthermore, she is also interested in foundational optimization theories.
ude.csu@zecarg Website
Grace is generally interested in efficient robot learning methods by using prior multi-task data or learned policies. Specifically, her research focuses on efficient multi-task learning through shared behaviors and efficient inverse reinforcement learning using multi-task data.
Master’s Students
Undergraduate Students
