Yali-Zheng-intelligent sensing informatics-Best Researcher Award
Shenzhen Technology University-China
Author Profile
Early Academic Pursuits
Dr. ZHENG Yali began his academic journey with a Bachelor's degree in Electronic Science and Technology from Beijing Jiaotong University, followed by a Master's in Microelectronics and Solid Electronics from Peking University. He completed his Doctor of Philosophy in Electronic Engineering at The Chinese University of Hong Kong in 2014.
Professional Endeavors
Dr. ZHENG has gained diverse experiences in academia and research. He started as a Research Assistant at The Chinese University of Hong Kong before becoming a Postdoctoral Fellow in the Department of Surgery, Faculty of Medicine. He later transitioned to an Assistant Professor role at Shenzhen Technology University, where he has been serving since 2019. Currently, he holds the position of Tenured Associate Professor in the Department of Biomedical Engineering.
Contributions and Research Focus
Dr. ZHENG's research focus lies in wearable medical devices, intelligent sensing informatics, and biomedical system modeling. His significant contributions include the development of deep learning models for continuous cuffless blood pressure estimation, wearable systems for health monitoring, and the application of artificial intelligence in medical diagnostics. His research projects, such as "Investigation of the representation learning of physiological signals" and "Wearable and Continuous Cuffless Blood Pressure Monitoring," showcase his commitment to advancing healthcare technologies.
Research Grants
Dr. ZHENG has successfully secured research grants for several innovative projects, demonstrating his capability in obtaining funding for cutting-edge research. Noteworthy projects include the development of a deep learning model for non-stationary blood pressure estimation and the creation of a flexible and intelligent wearable system for continuous cuffless blood pressure monitoring.
Publications
Dr. ZHENG has made significant contributions to scholarly literature with numerous journal papers and conference presentations. His publications cover a wide range of topics, including the development of lightweight deep neural networks, novel indicators for peripheral arterial disease assessment, and the use of wearable-based pulse arrival time for characterizing the vascular system.
Academic Services
Dr. ZHENG actively engages in academic services, serving on committees related to wearable biomedical sensors and systems. He has been a peer reviewer for reputable journals, showcasing his commitment to maintaining high research standards.
Awards, Invited Talks, and Recognition
Dr. ZHENG has received accolades for his work, including awards such as the Best Researcher Award in Human-Computer Interaction and Augmented Reality. He has delivered invited talks at prestigious events, sharing his expertise on topics like cuffless blood pressure monitoring and wearable medical devices. His involvement in various academic activities reflects his leadership and influence in the field.
Impact and Influence
Dr. ZHENG's work has left a notable impact on the fields of wearable technology, biomedical engineering, and health informatics. His research, publications, and contributions to conferences contribute to the advancement of knowledge in these domains.
Legacy and Future Contributions
Dr. ZHENG's legacy lies in his innovative research and dedication to improving healthcare through technology. His future contributions are anticipated to further push the boundaries of wearable medical devices, intelligent sensing, and biomedical modeling, leaving a lasting impact on the academic and technological landscape.
Notable Publication
- Tiny-PPG: A lightweight deep neural network for real-time detection of motion artifacts in photoplethysmogram signals on edge devices
- Novel and robust auxiliary indicators to ankle-brachial index using multi-site pulse arrival time and detrended fluctuation analysis for peripheral arterial disease assessment
- Characterization of the vascular system using overnight wearable-based pulse arrival time and ambulatory blood pressure: A pilot study
- Deep learning model with individualized fine-tuning for dynamic and beat-to-beat blood pressure estimation
- Beats-to-Beats Estimation of Blood Pressure during Supine Cycling Exercise Using a Probabilistic Nonparametric Method