About me

I am a Graduate Research Assistant in the Cognitive Optimization and Relational (CORE) Robotics lab, led by Prof. Matthew Gombolay. My research interests focus on graph-based policy learning that utilizes graph neural networks for representation learning and reinforcement learning for decision-making, with applications to human-robot team collaboration, multi-agent reinforcement learning and stochastic resource optimization. Prior to this, I was a GRA in GT-Bionics lab, where my research focused on computer vision for biomedical applications.

I am a Ph.D. student in the School of Electrical and Computer Engineering (ECE) at the Georgia Institute of Technology. I received the B.S. degree and the M. E. degree, both in Electrical Engineering, from Shanghai Jiao Tong University. I also received the M.S. degree from ECE, Georgia Tech.


(June 2022) Our HybridNet paper for fast human-robot coordination is accepted to IROS 2022.

(May 2022) I will give a coding tutorial on graph neural networks for multi-robot coordination in AAMAS 2022. Try it yourself in Google Colab!

(Feb. 2022) Our AirME paper is accepted to ICAPS 2022.

(Dec. 2021) Our paper on differentiable binarized communication is accepted to AAMAS 2022.

(Aug. 2021) Our preprint on learning heterogeneous communication for composite robot teams is out.

(June 2021) We have extended ScheduleNet to enable coordinating teams of heterogeneous robots in 2D areas, which is accepted to Autonomous Robots.

(June 2020) Our ScheduleNet paper will be presented virtually in RSS 2020.

(June 2020) Our paper on learning scheduling for multi-robot coordination is accepted to RA-L. Check our blog to learn more about it.

(Mar. 2018) Our system paper on automated behavior analysis is accepted to Medical & Biological Engineering & Computing.

(Mar. 2017) Our EnerCage-Homecage paper is accepted to IEEE Transactions on Biomedical Engineering.