Mr. Cenyu Liu | Hybrid Architecture | Best Researcher Award

Mr. Cenyu Liu | Master Student | Shanghai Jiaotong University | China

Academic Background

Cenyu Liu is a Master’s student at Shanghai Jiao Tong University, specializing in biomedical engineering and deep learning. His academic training encompasses advanced neural network design, signal processing, and wearable health technologies. Cenyu has focused on developing efficient deep learning models for automatic sleep stage classification from single-channel EEG signals. His work has been recognized in top journals and conferences, with citations across Google Scholar and Scopus, demonstrating the reach and influence of his research. He maintains an active researcher profile with indexed publications and a growing h-index, reflecting consistent contributions to biomedical AI and wearable device applications. Documentation of his research, including articles, patents, and profiles, is publicly accessible through ORCID and research profile links.

Research Focus

Cenyu Liu’s research centers on the intersection of artificial intelligence and healthcare technology. He develops compact hybrid deep learning models that enable accurate and efficient sleep stage classification for real-time monitoring using wearable devices. His work aims to bridge computational neuroscience and practical health applications, making AI solutions deployable on edge devices.

Work Experience

Cenyu has primarily conducted research in academic settings, collaborating with multidisciplinary teams at Shanghai Jiao Tong University. He has worked closely with experts in wearable sensor technology and biomedical signal processing, contributing to projects that integrate machine learning with portable health monitoring systems. His experience includes designing experiments, implementing deep learning pipelines, and validating models on benchmark datasets.

Key Contributions

Mr. Cenyu Liu has made significant contributions to AI-driven healthcare through the development of MultiScaleSleepNet, a hybrid CNN–BiLSTM–Transformer model that leverages multi-scale feature extraction and attention mechanisms for EEG-based sleep stage classification. His model demonstrates robustness across datasets and is optimized for computational efficiency, making it suitable for real-time applications on wearable devices. Additionally, he has contributed to mobile-based health monitoring patents and co-authored research on continuous core body temperature monitoring, enhancing the safety and efficiency of health-tracking systems.

Awards & Recognition

Cenyu Liu has been recognized for his research excellence and innovation in biomedical AI, particularly in wearable health technologies. His work has gained attention through peer-reviewed publications and citations in both Scopus and Google Scholar, reflecting its scientific impact. He has been invited to collaborate on high-profile projects that advance the practical application of AI in healthcare.

Professional Roles & Memberships

Mr. Cenyu is an active member of IEEE, participating in professional communities focused on artificial intelligence, biomedical engineering, and signal processing. He engages in collaborative research projects and maintains a profile of professional development through scholarly networks, contributing to the global scientific community.

Publication Profile

ORCID

Featured Publications

Liu, C., Guan, Q., Zhang, W., Sun, L., Wang, M., Dong, X., Xu, S. MultiScaleSleepNet: A Hybrid CNN–BiLSTM–Transformer Architecture with Multi-Scale Feature Representation for Single-Channel EEG Sleep Stage Classification. Sensors.

Zhang, W., Li, L., Wang, Y., Dong, X., Liu, C., Sun, L., Xu, S. Continuous Core Body Temperature Monitoring for Heatstroke Alert via a Wearable In-Ear Thermometer. ACS Sensors.

Impact Statement / Vision

Mr. Cenyu Liu envisions advancing artificial intelligence for personalized and portable healthcare. His research seeks to enable real-time, low-complexity AI models for wearable devices, empowering continuous health monitoring and improving preventive care through innovative computational solutions.