Keynote Speakers
We are honored to host these distinguished keynote speakers who will share their insights and expertise at ICBIP 2025.

Prof. Donghyun Kim(SPIE Fellow)
Yonsei University
Donghyun Kim received B.S. and M.S. from Seoul National University in Electronics Engineering. He graduated from the Massachusetts Institute of Technology with Ph.D. in electrical engineering in the area of novel multi-dimensional display technologies and smart optical filters. He worked on next generation fiber-optic access communication systems at Corning Inc. as a senior research scientist and then investigated cellular biophotonic sensors for cell-based assays at Cornell University as a postdoctoral fellow. He has been leading Biophotonics Engineering Laboratory of Yonsei University, Seoul, Korea. The main theme of his research at Yonsei has been focused on sensing and imaging applications in biomedical engineering based on near-field manipulation by plasmonic techniques and metastructures. He has given 100+ invited lectures on related topics and written more than 200+ peer-reviewed journal and conference publications on nano and biophotonics, many of which were the results of domestic and international collaboration. He also holds 30+ international patents. Dr. Kim served as Undergraduate Chair of the School of Electrical Engineering of Yonsei University and chaired international conferences including CLEO Pacific Rim 2024. He is currently Vice President of the Optical Society of Korea. He is a Fellow of the SPIE and a Senior Member of the IEEE.rt in medical image analysis and machine learning applications in healthcare. Her research focuses on developing novel algorithms for early disease detection using medical imaging data.
Keynote Talk: "Metaplasmonics for Advanced Biomedical Sensing and Imaging Applications"

Prof. Haiteng Jiang
Zhejiang University
Jiang Haiteng is a National High-Level Young Talent, Chief Young Scientist of the China Brain Project, and Director of the Clinical Base Office of the National Key Laboratory of Brain-Machine Intelligence. He is a PI at MOE Frontier Science Center for Brain Science and Brain-Machine Integration, the Affiliated Mental Health Center of Zhejiang University School of Medicine and Liangzhu Laboratory. His team has been dedicated to developing novel brain information analysis methods, exploring cognitive and brain disease mechanisms, and facilitating clinical translation. He currently leads three national-level projects and his representative research findings have been published in high-impact journals such as Advanced Science, Annals of Neurology, and Neuropsychopharmacology as a corresponding or first author. He has received several awards, including the LiZao Neuroscience Award from Zhejiang University School of Medicine, the Emerging Countries Travel Scholar Award at the 21st International Conference on Biomagnetism, and the 2018 Neuroscience Society Hot Topic Award.
Keynote Talk: "Translational Psychiatry with Real-World Clinical EEG"
Abstract: Real-world data (RWD) research involves the analysis of practical, heterogeneous data collected during routine clinical practice. Unlike controlled trial data, RWD is characterized by diversity, non-structural formats, complexity, and imperfections, posing challenges but also offering valuable insights into real clinical scenarios. Electroencephalography (EEG), a non-invasive technique for monitoring brain electrical activity, has become a critical tool in diagnosing and researching neurological and psychiatric disorders such as epilepsy, depression, and sleep disorders. This report highlights our research group’s progress in leveraging deep learning (DL) and transfer learning (TL) techniques to automate sleep staging and enhance the precision of psychiatric disorder diagnoses using real-world EEG and polysomnography (PSG) data. Sleep staging, traditionally a labor-intensive manual process, benefits from DL’s ability to extract complex patterns from raw EEG signals, improving accuracy and efficiency. Meanwhile, TL enables the adaptation of pre-trained models to diverse patient populations, addressing data scarcity and variability in clinical settings. By integrating real-world EEG and PSG data, our work aims to bridge the gap between research and clinical application, ultimately advancing personalized medicine and improving diagnostic outcomes for sleep and psychiatric disorders.

Prof. Dakun Lai
University of Electronic Science and Technology of China (UESTC)
Professor Lai is currently the director of the Biomedical Imaging and Electrophysiology Lab at the University of Electronic Science and Technology of China (UESTC), and the Vice Chairman of Chinese Heart Rhythm Society (CHRS). He received his Ph.D. in Medical Electronics from Fudan University in 2008. Then he completed a three-year Postdoctoral Associate in Biomedical Engineering at the University of Minnesota, USA. From 2012, he has been on the faculty of the School of Electronic Science and Technology, UESTC, China, where he was appointed as a Professor of Electrical Science and Technology. Dr. Lai is members of IEEE and the Engineering in Medicine and Biology Society, and the member of American Heart Associate. He is also an editor and reviewer of several international journals. He has published 80 peer-reviewed papers in Circulation, Physics in Medicine and Biology, IEEE TITB, IEEE Sensors J etc. and holds 30 Chinese Patents. His research interests and main contributions include medical electronics, bioelectromagnetism and cardiac/nural electrophysiology. He has pioneered the development of various wearable ECG devices with flexible dry electrodes and made significant contributions to deep learning based bioelectrical signal analysis, and detection and prediction of severe cardiac arrhythmias and neuro-disorder.
Keynote Talk: "AI-Enabled Wearable Electronics in Cardiology"
Abstract: This presentation focuses on AI-enabled wearable electronics in cardiology. It first elaborates on the clinical background and technological challenges in cardiology, laying the groundwork for the application needs of wearable devices. Then, it analyzes the development of wearable electronics, from breakthroughs in dry electrode technology to the evolution of big data collection capabilities. Next, it explores the transformation path of artificial intelligence from basic concepts to clinical bedside implementation in cardiology. Finally, it discusses the major opportunities and challenges in this field, aiming to present the transformative potential and unresolved issues brought by the integration of AI and wearable technology in cardiology, providing insights for related research and clinical applications.