2024年10月 — 今：中国科学院计算技术研究所，副研究员

2022年8月 — 2024年9月：九坤投资，AI LAB，研究员

2020年9月 — 2022年8月：华为-北京研究所，诺亚方舟实验室，主任研究员（Principal Researcher）

2017年9月 — 2020年9月：华为-香港研究所，诺亚方舟实验室，高级研究员（Senior Researcher）

2012年1月 — 2017年8月：美国雪城大学（Syracuse University），电子与计算机工程专业，博士

2015年1月 — 2016年8月：美国雪城大学（Syracuse University），数学专业，硕士

2006年9月 — 2010年7月：北京理工大学，信息与电子学院，本科

期刊文章:

[1] Shaokang Dong, Hangyu Mao, Shangdong Yang, Shengyu Zhu, Wenbin Li, Jianye Hao, Yang Gao, WToE: Learning When to Explore in Multiagent Reinforcement Learning, IEEE Transactions on Cybernetics, 2024.

[2] Zhuangyan Fang**, Shengyu Zhu**, Jiji Zhang, Yue Liu, Zhitang Chen, Yangbo He, Low rank directed acyclic graphs and causal structure learning, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2024. (** equal contribution)

[3] Ran Chen, Shoubo Hu, Zhitang Chen, Shengyu Zhu, et al., A unified framework for layout pattern analysis with deep causal estimation, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2023.Zitong [4] Lu, Zhi Geng, Wei Li, Shengyu Zhu, Jinzhu Jia, Evaluating causes of effects by posterior effects of causes, Biometrika, 2022.（统计学四大期刊）

[5] Zhuangyan Fang, Yue Liu, Zhi Geng, Shengyu Zhu, Yangbo He, A local method for identifying causal relations under Markov equivalence, Artificial Intelligence (AIJ), 2022.

[6] Shengyu Zhu, Biao Chen, Zhitang Chen, and Pengfei Yang, Asymptotically optimal one- and two-sample testing with kernels, IEEE Transactions on Information Theory (TIT), April 2021.

[7] Shengyu Zhu and Biao Chen, Distributed detection in ad hoc networks through quantized consensus, IEEE Transactions on Information Theory (TIT), August 2018.

[8] Shengyu Zhu and Biao Chen, Quantized consensus by the ADMM: Probabilistic versus deterministic quantizers, IEEE Transactions on Signal Processing (TSP), April 2016.

[9] Ge Xu, Shengyu Zhu, and Biao Chen, Decentralized data reduction with quantization constraints, IEEE Transactions on Signal Processing (TSP), April 2014. (corresponding author)

会议文章：

[1] Ruiqi Zhao, Lei Zhang, Shengyu Zhu, Zitong Lu, Zhenhua Dong, Chaoliang Zhang, Zhi Geng, Yangbo He, Conditional counterfactual causal effect for individual attribution, UAI, 2023. (spotlight)

[2] Xiaoyu Tan, LIN Yong, Shengyu Zhu, Chao Qu, Xihe Qiu, Xu Yinghui, Peng Cui, Yuan Qi, Provably Invariance Learning without Domain Information”, ICML, 2023.

[3] Yong Lin, Shengyu Zhu, Lu Tan, Peng Cui, ZIN: When and how to learn invariance without environment partition?, NeurIPS, 2022. (spotlight; corresponding author)

[4] Junlong Lyu, Zhitang Chen, Chang Feng, Wenjing Cun, Shengyu Zhu, Yanhui Geng, Zhijie Xu, Para-CFlows: C^k-universal diffeomorphism approximators as superior neural surrogates, NeurIPS, 2022.

[5] Xiaopeng Zhang, Shoubo Hu, Zhitang Chen, Shengyu Zhu, et al., RCANet: Root cause analysis via latent variable interaction modeling for yield improvement, IEEE International Test Conference (ITC), 2022.

[6] Xinwei Shen, Shengyu Zhu, Jiji Zhang, Shoubo Hu, Zhitang Chen, Reframed GES with a neural conditional dependence measure, Conference on Uncertainty in Artificial Intelligence (UAI), 2022.

[7] Ruoyu Wang, Mingyang Yi, Zhitang Chen, Shengyu Zhu, Out-of-distribution generalization with causal invariant transformations, IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), June 2022. (corresponding author)

[8] Iganvier Ng, Shengyu Zhu, Zhuangyan Fang, Haoyang Li, Zhitang Chen, Jun Wang, Masked gradient-based causal structure learning, SIAM Conference on Data Mining (SDM), May 2022. (corresponding author)

[9] Ran Chen, Shoubo Hu, Zhitang Chen, Shengyu Zhu, et al., A unified framework for layout pattern analysis with deep causal estimation, IEEE/ACM International Conference On Computer Aided Design (ICCAD), November 2021.

[10] Xiaoqiang Wang, Yali Du, Shengyu Zhu, Liangjun Ke, Zhitang Chen, Jianye Hao, Jun Wang, Ordering-based causal discovery with reinforcement learning, International Joint Conference on Artificial Intelligence (IJCAI), July 2021. (corresponding author)

[11] Shengyu Zhu, Ignavier Ng, and Zhitang Chen, Causal discovery with reinforcement learning, International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, April 2020. (highest review score and oral presentation)

[12] Shengyu Zhu, Biao Chen, Pengfei Yang, and Zhitang Chen, Universal hypothesis testing with kernels: Asymptotically optimal tests for goodness of fit, International Conference on Artificial Intelligence and Statistics (AISTATS), Naha, Okinawa, Japan, April 2019.

[13] Shengyu Zhu and Biao Chen, Distributed detection over connected networks via one-bit quantizer, IEEE International Symposium on Information Theory (ISIT), Barcelona, Spain, July 2016.

[14] Shengyu Zhu and Biao Chen, Distributed average consensus with bounded quantization, IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Edinburgh, UK, July 2016.

[15] Shengyu Zhu, Mingyi Hong, and Biao Chen, Quantized consensus ADMM for multi-agent distributed optimization, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, March 2016.

[16] Shengyu Zhu and Biao Chen, Distributed average consensus with deterministic quantization: an ADMM approach, IEEE Global Conference on Signal and Information Processing (GlobalSIP), Orlando, FL, December 2015. (IEEE travel grant)

[17] Shengyu Zhu, Ge Xu, and Biao Chen, Are global sufficient statistics always sufficient: the impact of quantization on decentralized data reduction, Asilomar Conference on Signals, Systems, and Computers (Asilomar), Monterey, CA, November 2013. (invited paper)

[18] Shengyu Zhu and Biao Chen, Data reduction in tandem fusion systems, IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP), Beijing, China, July 2013.

[19] Shengyu Zhu, Earnest Akofor, and Biao Chen, Interactive distributed detection with conditionally independent observations, IEEE Wireless Communications and Networking Conference (WCNC), Shanghai, China, April 2013.

专利：

[1] “一种通信网络故障的根因定位方法及相关设备”，授权，CN113923099B（对应SDM’22论文）

[2] “一种通信方法及通信装置”，实质审查，CN116192330A （对应INFOCOM投稿论文）

[3] “一种数据处理方法及相关设备”，实质审查，CN115905932A （对应CVPR’22 论文）

[4] “一种芯片故障识别方法及相关设备”，授权，CN113657022B，（对应ICCAD’21 和TCAD’22论文）

[5] “缺陷根因确定方法、装置和存储介质”，实质审查，CN115238641A（对应ITC’22 论文）

优秀审稿人（Top Reviewer），UAI，2023

产品线重大挑战问题攻关奖（团队），2012 实验室，华为，2021

商业贡献奖（团队）, 诺亚方舟实验室，2012 实验室，华为，2021

“计算系统理论与技术委员会”优秀团队奖，2012 实验室，华为，2021

优秀实践奖（因果学习研究）, 2012 实验室，华为，2020

创新先锋一等奖，2012 实验室，华为，2020

总裁个人奖，网络产品线，华为，2019

优秀博士毕业生（All University Doctoral Prize），美国雪城大学，2018