CV

You can download a PDF copy of my CV here_english or here_chinese.

Education

• Mar. 2019 ~ present: Pursuing the combined master and Ph.D. degree in electronic engineering at Hanyang University, South Korea.
• Sep. 2013 ~ Jul. 2017: B.S. in Electronic and Information Engineering at Harbin University of Science and Technology, China.
• Sep. 2016 ~ May 2017: Exchange student at Chonnam National University, South Korea.

Research interests

My research interests are in the areas of reinforcement learning and artificial intelligence, pattern recognition and machine learning, multi-arm bandits, and Internet of things. In particular,

• Reinforcement learning for the wireless networks optimization
• Random access for the IoT nwtworks
• Bandit algorithms

Work experience

• May 2018 ~ Jan. 2019: Zhenhua Microelectronics Co., LTD, Shenzhen, Guangdong Province. R&D engineer.

Publications

A reinforcement learning based low-delay scheduling with adaptive transmission Permalink

Yu Zhao, Joohyun Lee. "A reinforcement learning based low-delay scheduling with adaptive transmission." 2019 International Conference on Information and Communication Technology Convergence (ICTC). 2019, pp. 916-919, doi: 10.1109/ICTC46691.2019.8939680.

다중 안테나 기술에서의 딥러닝 기반 직교 진폭 변조 디코더 Permalink

이동우， 자오유， 이주현. "다중 안테나 기술에서의 딥러닝 기반 직교 진폭 변조 디코더" 한국통신학회 학술대회논문집. 2021, pp. 107-108.

Reinforcement Learning for Random Access in Multi-cell Networks Permalink

Dongwoo Lee, Yu Zhao, Joohyun Lee. "Reinforcement Learning for Random Access in Multi-cell Networks." 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). 2021, pp. 335-338, doi: 10.1109/ICAIIC51459.2021.9415281.

Q-greedyUCB: A new exploration policy to learn resource-efficient scheduling Permalink

Yu Zhao, Joohyun Lee, Wei Chen. "Q-greedyUCB: A new exploration policy to learn resource-efficient scheduling." China Communications. vol. 18, no. 6, pp. 12–23, 2021.

Multi-Agent Reinforcement Learning for a Random Access Game Permalink

Dongwoo Lee, Yu Zhao*, Jun-Bae Seo, Joohyun Lee. "Multi-Agent Reinforcement Learning for a Random Access Game." IEEE Transactions on Vehicular Technology. vol. 71, no. 8, pp. 9119–9124, 2022.

NOMA-based Random Access: Multi-Agent Reinforcement Learning Mehtod Permalink

자오유, 서준배, 이주현. "NOMA기반 랜덤 엑세스: 다중 에이전트 강화 학습 방법으로" 한국통신학회 학술대회논문집. 2023, pp. 140-141.

Multi-Agent Deep Reinforcement Learning For Cross-Layer Scheduling in Mobile Ad-hoc Networks Permalink

Xinxing Zheng, Yu Zhao, Joohyun Lee, Wei Chen. "Multi-Agent Deep Reinforcement Learning For Cross-Layer Scheduling in Mobile Ad-hoc Networks." China Communications. vol. 20, no. 8, pp. 78–88, 2023.

SOQ: Structural Reinforcement Learning for Constrained Delay Minimization with Channel State Information Permalink

Yu Zhao, Yeongjin Kim, Joohyun Lee. "SOQ: Structural Reinforcement Learning for Constrained Delay Minimization with Channel State Information." IEEE Internet of Things Journal. Accepted.

Multi-Agent Reinforcement Learning for a Multichannel Uplink Random Access: Congestion Game Perspective Permalink

Yu Zhao, Joohyun Lee, Jun-Bae Seo. "Multi-Agent Reinforcement Learning for a Multichannel Uplink Random Access: Congestion Game Perspective." 2023 12th International Conference on Awareness Science and Technology (iCAST), Taichung, Taiwan, 2023, pp. 156-160.

Talks

Multi-agent Reinforcement Learning for a Random access Game

June 20, 2022

Talk at Oak vally, Wonju, Gangwon-do

NOMA-based Random Access: Multi-agent Reinforcement Learning Method

February 08, 2023

talk at Yongpyeong Resort, Pyeongchang, Gangwon-do

Ad-hoc cross-layer scheduling: Multi-agent Deep Reinforcement Learning Method

June 19, 2023

Talk at Oka vally, Wonju, Gangwon-do

Deep Reinforcement Learning and Its Applications

August 31, 2023

Talk at Hanyang University, Ansan, Gyeonggi-do

Teaching

Teaching experience 1

Teaching experience 2

Service and leadership