Ying Wen

Biography

Ying Wen is now a tenure-track associate professor in School of Artificial Intelligence and John Hopcroft Center, Shanghai Jiao Tong University. His research interests include deep reinforcement learning, multi-agent systems, game theory and machine learning systems. He received his PhD in the Department of Computer Science, University College London in 2020. Before that, Ying earned his MRes(Master of Research) with Distinction Honor from University College London in 2016 and B.Eng. with First Class Honor from Queen Mary, University of London and Beijing University of Posts and Tel. in 2015. He was an intern at Huawei, MediaGamma, Amazon and Baidu.

[05/24] One paper gets accepted in JAIR.

[05/24] Two papers get accepted in ICML 2024.

[04/24] One paper gets accepted in SIGIR 2024.

[12/23] One paper gets accepted in AAAI 2024.

[05/23] Two papers get accepted in ICML 2023.

[04/23] One paper gets accepted in FCS.

[02/23] A white paper about large-scale pre-trained and multi-agent reinforcement learning.

[01/23] One paper gets accepted in ICLR 2023.

[04/22] One paper gets accepted in ACL 2022.

[04/22] One paper gets accepted in IEEE TWC.

[01/22] One paper gets accepted in ICLR 2022.

[09/21] Two papers get accepted in NeurIPS 2021.

[06/21] Open sourced MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning

[05/21] Two papers get accepted in ICML 2021.

[05/21] Awarded a Grant from the Shanghai Sailing Program, by STCSM.

[01/21] One paper gets accepted in AAMAS 2021.

[01/21] One paper gets accepted in ICC 2021.

[11/20] One paper gets accepted in CoRL 2020.

[06/20] One paper gets accepted in ICML 2020.

[05/20] One paper gets accepted in IJCAI 2020.

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Ming Zhou, Ziyu Wan, Hanjing Wang, Muning Wen, Runzhe Wu, Ying Wen, Yaodong Yang, Weinan Zhang, Jun Wang. MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning. Preprint, 2021.

Yaodong Yang, Jun Luo, Ying Wen, Oliver Slumbers, Daniel Graves, Haitham Bou Ammar, Jun Wang, Matthew E. Taylor. Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems. Blue Sky Track, AAMAS, Best Paper Award, 2021.