Dongzhou Cheng I am a third-year graduate student at the School of Electrical and Automation Engineering, Nanjing Normal University, supervised by Prof. Lei Zhang . My research interests is building Effective and Efficient Sensing Systems, with a primary focus on developing privacy-preserving machine learning like Federated Learning and Self-Supervised learning for Smart Health applications. Email  /  CV  /  Google Scholar  /  Github

Major Publications

	MaskCAE: Masked Convolutional AutoEncoder via Sensor Data Reconstruction for Self-Supervised Human Activity Recognition Dongzhou Cheng, Lei Zhang, Can Bu, Lutong Qin, Shuoyuan Wang, Hao Wu and Aiguo Song. [IEEE JBHI] IEEE Journal of Biomedical and Health Informatics (IF: 7.7, TOP), 2024 The first Masked Convolutional AutoEncoder for HAR (Pure Convolutional Architecture!) [Paper] [Code]
	Learning hierarchical time series data augmentation invariances via contrastive supervision for human activity recognition Dongzhou Cheng, Lei Zhang, Can Bu, Hao Wu and Aiguo Song. [KBS] Knowledge-Based Systems (IF: 8.8, TOP), 2023 It's an interesting work, at least for me :) [Paper] [Code]
	ProtoHAR: Prototype Guided Personalized Federated Learning for Human Activity Recognition Dongzhou Cheng, Lei Zhang, Can Bu, Xing Wang, Hao Wu and Aiguo Song. [IEEE JBHI] IEEE Journal of Biomedical and Health Informatics (IF: 7.7, TOP), 2023 The New federated learning method for HAR! [Paper] [Code]

Other Publications

• I have eight more papers that are being reviewed or undergoing major revisions.
IEEE Sensors, IEEE TMC, IEEE JBHI, IEEE TIM, ESWA, KBS, IPM.

• Two works are in progress.

Selected Awards

• National scholarship, 2023
• I ranked First while pursuing my master's degree (1/150+, 2023).
• I ranked First in my undergraduate study (1/36, 2019).
• The code (200+ stars) I implemented was reproduced by some Official Accounts such as 极市平台, CVer, PaperWeekly, and CVHub.
• Participated in one National and two Provincial College Students' Innovative Entrepreneurial Training Plan Program.
• Achieved the best accuracy (No.1 of 30+ teams) in the Huawei Cloud Food Recognition Competition, 2019. Bonus: 6000 RMB.

Academic Experience

Invited Reviewer

• Neural Network (IF:7.8)
• Knowledge-based Systems (IF:8.8)
• Expert Systems With Applications (IF:8.5)
• Artificial Intelligence Review (IF:12.0)
• Intertnet of Things (IF:5.9)

Supervision of Some Papers.

• Jianglai Yu (Mater, NNU, IE, 2022)
• Minghui Yao (Mater, NNU, IE, 2022)
• Songming Sun (Mater, NNU, IE, 2022)
• Quanbing Wang (Mater, NNU, IE, 2022)