Qingyu Liu, Ph.D. Assistant Professor The School of Electronic and Computer Engineering Peking University

Contact

Office: 310 Building B, Peking University Shenzhen Graduate School, 2199 Lishui Road, Nanshan, Shenzhen, Guangdong, China

Email: qy.liu AT pku.edu.cn

Home page: https://qyliu19.github.io/

Research Interests

• Wireless networking
• Mobile networking
• Edge AI
• Internet of Things
• Intelligent transportation

News

• [May 2024] I will serve on the TPC of IEEE GLOBECOM 2024.
• [Mar. 2024] Our paper "Aequitas: A 5G Scheduler for Minimizing Outdated Information in IoT Networks" has been accepted by IEEE Internet of Things Journal.
• [Feb. 2024] I will serve on the TPC of IEEE/ACM IWQoS 2024.
• [Jan. 2024] I will serve as Secretary of the IEEE ComSoc Asia/Pacific Region Board.
• [Aug. 2023] Our paper "Age of Critical Information: Optimizing Data Freshness Based on Content Criticality" has been accepted by MILCOM 2023.
• [Jun. 2023] I will serve on the TPC of IEEE GLOBECOM 2023.
• [Jun. 2023] I joined the School of Electronic and Computer Engineering at Peking University as an Assistant Professor.
• [April 2023] I will serve on the TPC of IEEE INFOCOM 2024.
• [Mar. 2023] I was awarded Distinguished Member of the 2023 INFOCOM Technical Program Committee.
• [Jan. 2023] Our paper "Ride the Tide of Traffic Conditions: Opportunistic Driving Improves Energy Efficiency of Timely Truck Transportation" has been accepted by IEEE Transactions on Intelligent Transportation Systems.
• [Dec. 2022] Our paper "Eywa: A General Approach for Scheduler Design in AoI Optimization" has been accepted by IEEE INFOCOM 2023.
• [Nov. 2022] Our paper "Wireless Scheduling to Optimize Age of Information based on Earliest Update Time" has been accepted by IEEE Internet of Things Journal.
• [Oct. 2022] Our paper "Aion: A Bandwidth Conserving Scheduler with Data Freshness Guarantee" has been accepted by IEEE Transactions on Mobile Computing.
• [Oct. 2022] I will serve on the TPC of ICCCN 2023.
• [Sep. 2022] Our paper "Hector: A Reinforcement Learning-Based Scheduler for Minimizing Casualties of a Military Drone Swarm" has been accepted by MILCOM 2022.