I am a tenure-track Assistant Professor in the Department of Computer Science, Peking University.
Before joining Peking University in September 2017, I was a postdoc in the Department of Computer Science and Engineering, Pennsylvania State University. I received the PhD degree from the School of Computer Science and Engineering, Nanyang Technological University in April 2014, master and bachelor degrees from Southeast University.
My research interests fall at the intersection of distributed systems, multi-agent reinforcement learning, and artificial intelligence, and I currently focus on
I am looking for self-motivated undergraduate students for research internships. I am recruiting PhD students, starting Fall 2019, and also postdoc researchers. If you are interested, drop me an email.
Our paper “Learning Attentional Communication for Multiagent Cooperation” was accepted at NIPS’18. Congratulations to Jiechuan Jiang.
Our paper “CrowdVision: A Computing Platform for Video Crowdprocessing Using Deep Learning” was accepted at IEEE Transactions on Mobile Computing.
Our paper “A Computing Platform for Video Crowdprocessing Using Deep Learning” was accepted at the IEEE International Conference on Computer Communications (INFOCOM) 2018.
ATOC Biologically, communication is closely related to and probably originated from cooperation. For example, vervet monkeys can make different vocalizations to warn other members of the group about different predators. Similarly, communication can be crucially important in multi-agent reinforcement learning (MARL) for cooperation, especially for the scenarios where a large number of agents work in a collaborative way, such as autonomous vehicles planning, smart grid control, and multi-robot control. MARL can be simply seen as independent reinforcement learning (RL), where each learner treats the other agents as part of its environment. [Read More…]
The vast adoption of mobile devices with cameras has greatly assisted in the proliferation of the creation and distribution of videos. Videos, which are a rich source of information, can be exploited for on-demand information retrieval. Deep learning using Convolutional Neural Networks (CNNs) is state of the art computer vision techniques that can be used for information retrieval. However, due to the high computation of video processing using CNNs, it is not feasible or costs too much to process all videos at a centralized entity, considering a large set of videos which is common in this big data epoch. [Read More…]
MM 2017 2018
INFOCOM 2016 2019