Ph.D. Candidate @ BUAA
Former Embedded Software Engineer @ HaierI am a Ph.D. student at Beihang University. From 2018 to 2023, I worked as an Embedded Software Engineer at Haier Group while completing a Master's degree at Shandong University, supervised by Prof. Ju Liu. I previously studied at TU Ilmenau in Germany and hold a Bachelor's degree from Qingdao University of Science and Technology.
My research focuses on enhancing the reliability and efficiency of machine learning, with particular emphasis on enhancing model robustness against adversarial examples and improving training efficiency through techniques such as dataset distillation.
I am currently seeking internship opportunities and research collaborations. Please feel free to contact me via email.
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Beihang UniversitySchool of Electronic and Information Engineering
Ph.D. Candidate in Electronic EngineeringSep. 2023 - present -
Shandong UniversityM.Eng. in Electronic EngineeringSep. 2020 - Jun. 2023 -
TU IlmenauVisiting Student in Electronic EngineeringOct. 2016 - Oct. 2018 -
Qingdao University of Science and TechnologyB.Eng. in Mechanical Engineering and AutomationSep. 2012 - Jun. 2016
Experience
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Haier GroupEmbedded Software EngineerOct. 2018 - Jun. 2023
Honors & Awards
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Top Reviewer at NeruIPS 2024Sep. 2023
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Silver Award at ASCEND Competition for Re-IDOct. 2022
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Oral Presentation at International Conference on Swarm Intelligence (ICSI 2022)Sep. 2022
News
Services
Conference Reviewer
Journal Reviewer
Academic Talks
Selected Publications (view all )
ROME is Forged in Adversity: Robust Distilled Datasets via Information Bottleneck
Zheng Zhou, Wenquan Feng, Qiaosheng Zhang, Shuchang Lyu, Qi Zhao, Guangliang Cheng
International Conference on Machine Learning (ICML) 2025 Poster (Acceptance Rate: 26.9%, 3,260/12,107)
We introduce ROME, a method that enhances the adversarial robustness of dataset distillation by leveraging the information bottleneck principle, leading to significant improvements in robustness against both white-box and black-box attacks.
ROME is Forged in Adversity: Robust Distilled Datasets via Information Bottleneck
Zheng Zhou, Wenquan Feng, Qiaosheng Zhang, Shuchang Lyu, Qi Zhao, Guangliang Cheng
International Conference on Machine Learning (ICML) 2025 Poster (Acceptance Rate: 26.9%, 3,260/12,107)
We introduce ROME, a method that enhances the adversarial robustness of dataset distillation by leveraging the information bottleneck principle, leading to significant improvements in robustness against both white-box and black-box attacks.
BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation
Zheng Zhou, Wenquan Feng, Shuchang Lyu, Guangliang Cheng, Xiaowei Huang, Qi Zhao
Under review. 2024
BEARD is a unified benchmark for evaluating the adversarial robustness of dataset distillation methods, providing standardized metrics and tools to support reproducible research.
BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation
Zheng Zhou, Wenquan Feng, Shuchang Lyu, Guangliang Cheng, Xiaowei Huang, Qi Zhao
Under review. 2024
BEARD is a unified benchmark for evaluating the adversarial robustness of dataset distillation methods, providing standardized metrics and tools to support reproducible research.
BACON: Bayesian Optimal Condensation Framework for Dataset Distillation
Zheng Zhou, Hongbo Zhao, Guangliang Cheng, Xiangtai Li, Shuchang Lyu, Wenquan Feng, Qi Zhao
Under review. 2024
This work presents BACON, the first Bayesian framework for Dataset Distillation, offering strong theoretical support to improve performance.
BACON: Bayesian Optimal Condensation Framework for Dataset Distillation
Zheng Zhou, Hongbo Zhao, Guangliang Cheng, Xiangtai Li, Shuchang Lyu, Wenquan Feng, Qi Zhao
Under review. 2024
This work presents BACON, the first Bayesian framework for Dataset Distillation, offering strong theoretical support to improve performance.