个人简历

卢宗青现任北京大学计算机系数字媒体研究所研究员（“博雅青年学者”），“决策智能”课题组负责人。他于2014年在新加坡南洋理工大学获得计算机博士学位，2014至2017年在美国宾州州立大学从事博士后研究，并于2017年9月加入北京大学。他在东南大学获得学士和硕士学位。担任NeurIPS、IJCAI、AAMAS、INFOCOM等会议TPC，Nature Machine Intelligence等审稿人。
Current Projects
Learning to Cooperate
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…]
Reinforcement Learning, Multiagent Learning
Distributed Video Processing Using Deep Learning on Networked Devices
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…]
Deep Learning, Edge Computing
Past Projects
Building Smartphone Networks
Smartphones have great networking capabilities. They can access the Internet through cellular networks or wireless access points and communicate with nearby devices using WiFi Direct or Bluetooth. However, these network functions may not work in some circumstances where cellular towers and network infrastructure are destroyed, e.g. in disaster recovery. Nevertheless, communications in such scenarios are very important, and hence, in this research, we aim to build smartphone networks to provide communications without relying on cellular networks, wireless access points, or network infrastructure. [Read More…]
Smartphones, Opportunistic Networking, Data Offload
Health Sensing Using Mobile Devices
Mobile devices, such as smartphones, have become commonplace in health care settings, leading to the development of both platforms and applications for health care, e.g., HealthKit on iOS, where apps can collect users’ health and activity data and the data will be used for medical research to bring more powerful health solutions. However, no data is collected for the research of infectious diseases. Moreover, currently, most health data are collected by manual input or external devices. [Read More…]
Infectious Diseases, Human Contact Networks, Respiratory Symptoms, Smartphones
Exploring Social Structure for Network Designs
The proliferation of mobile devices, such as smartphones and tablets, and the popularity of online social networks that link humans, mobile devices and Internet together, increasingly emphasize the role of human behaviors on network designs. Due to the involvement of human behaviors, social structure provides crucial information of network structure and node organization, and thus can be exploited for network designs, e.g., in online social networks and mobile social networks. [Read More…]
Social Networks, Community, Information Diffusion
Teaching
Undergraduate Courses
Algorithms, Spring 2019, Spring 2020
Data Structures and Algorithms, Spring 2018
Introduction to Computer Systems, Fall 2017
Gradudate Courses
Deep RL and Multi-Agent RL, Spring 2020
Services
Technical Program Committee Member
INFOCOM 2016 2019 2020
IJCAI 2020
AAMAS 2020
MM 2017 2018
Session Chair
INFOCOM 2016, Session of Online Social Networks
Conference Organization
INFOCOM 2020 Worksop on Network Intelligence, General Co-Chair
ACM TURC 2018, Award Co-Chair
Contact
Room 523, Yanyuan Building, Peking University, Beijing, 100871, China.

研究领域

主要研究方向为（多智能体）强化学习、移动/边缘智能系统。"研究领域：强化学习，移动/边缘智能系统"

近期论文

Recent Publications
More Publications
[ICLR'20] Graph Convolutional Reinforcement Learning
Jiechuan Jiang, Chen Dun, Tiejun Huang and Zongqing Lu
International Conference on Learning Representation (ICLR), April 26-30, 2020. (Acceptance Rate: 26.5%=687⁄2594)
Details PDF Video Code Project
[AAAI'20] Generative Exploration and Exploitation
Jiechuan Jiang and Zongqing Lu
Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), February 7-12, 2020. (Acceptance Rate: 21%=1591⁄7737)
Details PDF Project ICML'19 Workshop on ERL (Spotlight Talk)
[NIPS'19] Learning Fairness in Multi-Agent Systems
Jiechuan Jiang and Zongqing Lu
Thirty-Third Annual Conference on Neural Information Processing Systems (NIPS), December 8-14, 2019. (Acceptance Rate: 21%=1428⁄6743)
Details PDF Video Code Project