ICIC 2024 Plenary Speakers

  • Shengyong Chen
  • Xiaoyan Chen
  • Dong Ming
  • Peiwu Qin
  • Ying Qu
  • Liyanage C De Silva
    •

    Shengyong Chen
    PhD & Professor, Vice President, IET Fellow, Tianjin University of Technology
    Xiaoyan Chen
    PhD & Professor, IEEE Member, Tianjin University of Science and Technology
    Dong Ming
    PhD & Professor, Vice President, Medical School, Tianjin University, China
    Peiwu Qin
    PhD & Professor, Tsinghua Shenzhen International Graduate School, Shenzhen, China
    Ying Qu
    PhD & Professor, Faculty of Geographical Science, Beijing Normal University, China
    Liyanage C De Silva
    PhD & Professor, Dean, School of Digital Science (SDS), Universiti Brunei Darussalam (UBD), Brunei Darussalam

    Smart Healthcare by Learning of Data: Understanding of Medical Images and Signals

    Shengyong Chen, PhD & Professor, Vice President, IET Fellow
    Tianjin University of Technology
    Email: csy@tjut.edu.cn

    Abstract: The talk presents a brief overview of Smart Healthcare, focusing on the understanding of medical images and signals. It first discusses the current state of healthcare, highlighting challenges and the need for an intelligent system. The report then delves into medical image understanding, emphasizing the importance of AI-driven techniques for diagnosis and prediction using advanced imaging modalities. Key topics include microscopic image analysis of blood cells, clump cells in pleural fluid, and cancer cells, as well as 3D analysis of cerebral vasculature and abdominal aortic aneurysms. The report also touches upon retinal image analysis for microaneurysm detection and surgical navigation techniques for abdominal aortic aneurysm surgeries. In all, the report emphasizes the potential of data-driven and AI-assisted approaches in enhancing diagnostic accuracy, facilitating early disease detection, and personalizing healthcare services.

    Bio-Sketch: Prof. Shengyong Chen received the Ph.D. degree from City University of Hong Kong in 2003. He worked as a guest researcher at University of Hamburg, Germany, where he received a fellowship from the Alexander von Humboldt Foundation in 2006. He was a visiting professor at Imperial College London, from 2008 to 2009. He is currently a full Professor and Vice-President in Tianjin University of Technology. He is an IET Fellow and an IEEE senior member. His research interests include machine vision and robotics. He received the National Outstanding Youth Foundation Award of NSFC. He has applied over 100 patents and published over 400 scientific papers, including 100 in IEEE Transactions, and 5 Best Paper Awards from international organizations. His work received over 15000 citations in Google Scholar.


    Research and Applications of Computer Vision in Intelligent Security System

    Xiaoyan Chen, PhD & Professor
    Tianjin University of Science and Technology, Tianjin, China
    Email: cxywxr@tust.edu.cn

    Abstract: In recent years, the rapid advancement of deep learning and computer vision technologies has significantly impacted various domains, particularly in enhancing intelligent security systems. Our research team, comprising experts from both academic and industrial backgrounds, has been dedicated to exploring and developing innovative computer vision solutions to address the growing demands of intelligent security. This abstract outlines our key research areas, including image super-resolution, low-light image enhancement, object detection, and highlights the practical applications of our work in real-world security systems.

    Bio-Sketch: Xiaoyan Chen is Professor of College of Electrical Information and Automation of Tianjin University of Science and Technology and Chief scientist of Shenzhen Soft Technology Co., LTD. She focuses on theoretical research of digital image processing with advanced techniques and their application to real world scenes. Except the traditional image processing and reconstruction, she devotes herself in novel approaches of machine learning and deep learning to address the target detection at low-light or low-spatial resolution cases. She is the reviewers of several famous journals of IEEE and the member of IEEE. She also is the director of the Life Electronics Branch of the Chinese Electronics Society, the director of the Tianjin Electronics Society and the Tianjin Instrumentation Society. She was the visiting scholar of Rensselaer Polytechnic Institute in USA, University of Kent in UK and the postdoctoral fellow at Tianjin University. She once won a second prize of Tianjin Science and Technology Progress Award and a third prize of Tianjin Technology Invention Award. She has published more than 100 academic papers in which 30+ are cited by Science Citation Index.


    Development and Challenges of Non-invasive Brain- Computer Interface

    Dong Ming, PhD & Professor, Vice President
    Medical School, Tianjin University, China
    Email: richardming@tju.edu.cn

    Abstract: Brain-Computer interaction (BCI) represents the pinnacle of human-machine interaction and is an essential pathway for the integration of artificial intelligence into biological intelligence. The technique has emerged as a new favorite in the realm of brain science research, bearing significant implications for advancing modern medicine. This presentation initially provides an overview of BCI by summarizing the common developmental trends of both invasive and non-invasive types. Subsequently, the complete technological chain of BCI, including neural foundations, sensing methods, software and hardware systems, key technologies, and typical applications will be addressed. The presentation will also introduce the latest research progress of the neural engineering group of Tianjin University in the field and provides a summary of the developmental trends and future directions of BCI at the end.

    Bio-Sketch: Professor Dong Ming, Vice President of Tianjin University, Chair Professor. He was a winner of Distinguished Young Scholars of the National Nature Science Fund, National Special Support Plan for High-level Talents. He serves as member of the Science and Technology Committee of the Ministry of Education and holds prestigious positions including Director of the National Health and Medical Big Data Research Institute, Director of the Intelligent Medical Engineering Research Center at the Ministry of Education, and Executive Director of the Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration. He is also a Life Member of the International Functional Electrical Stimulation Society (IFESS), the Vice Chairman of the Chinese Society of Biomedical Engineering, and the Vice Chairman of the Brain-Computer Interface and Brain-like Intelligence Professional Committee at the China Standardization Association. Prof. Ming’s research expertise concerns the foundational theories and critical technologies of intelligent human-machine interaction. He has also advanced substantial engineering applications of intelligent systems. His work have been recognized with the IOP Highly Cited Award, featured in JNE Highlight, JNER Highly Accessed, and covered by IEEE TBME and JBHI and reported by special issues of Science and Nature.


    Data Driven Technologies for Adolescent Big Health

    Peiwu Qin, PhD & Professor
    Tsinghua Shenzhen International Graduate School, Tsinghua University
    Email: pwqin@sz.tsinghua.edu.cn

    Abstract: Adolescent health crisis threatens the national security, demanding novel technologies for early diagnosis and intervention. In China, 83% high school students have myopia and 17.3% adolescence have mental disorders. We develop three-in-one myopia screening instrument that can provide axial length, refractive index, and retina fundus simultaneously, which is cheaper, efficient, and sustainable. Previously, people have to carry three different instruments with three staff to acquire these three parameters for myopia screening, which is time-consuming, labor-intensive, and expensive. The current questionnaire screening for mental disorder is subjective, non-reproducible, and inaccurate. We design an android application with mini-games and chat recording deployed in a portable robot. We propose a generalized model called GAME (Generalized Model with Attention and Multimodal EmbraceNet), which can diagnose adolescent mental conditions with high accuracy. We design novel material for sensitive EEG, EMG, and ECG data acquisition and propose RPA-CRISPR for biomarker detection. We create the first interactive canine brain atlas to facilitate the structure-function study of brain and propose hypergraph learning to parcellate cerebral cortex according to the cellular structure and organization pattern. To develop techniques for the health safeguard of next generation is the lab focus.

    Bio-Sketch: Dr. Peiwu Qin received the B.Sc. degree in biotechnology from Northeastern Agricultural University, Harbin, China, in 2002, the M.Sc. degree in developmental biology from Chinese Academy of Sciences, Beijing, China, in 2005, the M.Sc. degree in physical chemistry from Northeastern University, Boston, USA, in 2008, the M.Sc. degree in statistics from University of Missouri, Columbia, Missouri, USA, in 2013, and the Ph.D. degree in biochemistry from University of Missouri, Columbia, Missouri, USA, in 2013. He has been a Post-Doctoral Fellow with department of physics, University of California, Berkeley from 08/2013 to 08/2018. He was assistant professor at Tsinghua-Berkeley Shenzhen Institute from 09/2018-01/2022 and promoted to associate professor at Tsinghua Shenzhen International Graduate School, Shenzhen, China. He has published more than 70 SCI papers with more than 24 M RMB competitive funding support. He changed his research focus from single molecule biophysics to adolescent big health since 2018 and study myopia and mental illness screening device and corresponding computational methods for their diagnosis and prediction.


    Emergency Disaster Response with Satellite Imagery

    Ying Qu, PhD & Professor
    Faculty of Geographical Science, Beijing Normal University, China
    Email: yingqu@bnu.edu.cn

    Abstract: The intensification of unprecedented extreme climate events, such as hurricanes and floods, alongside catastrophic events like regional conflicts, poses significant challenges to emergency response systems worldwide. Traditional emergency disaster response heavily relies on survey-based assessments, which often lack real-time and comprehensive situational awareness, leading to less effective decision-making. Recently, the integration of satellite imagery with deep learning has emerged as a promising approach to enhance emergency disaster response. In this presentation, I will discuss how satellite imagery enables rapid and accurate assessments of disaster impacts, such as evaluations of urban destruction from conflicts and crop losses due to natural disasters like floods. These efforts hold the potential to improve the effectiveness of response measures by enabling precise resource allocation, strategic planning, and timely intervention. Ultimately, these efforts aim to contribute to resilient and efficient disaster management practices.

    Bio-Sketch: Ying Qu is a professor in the Faculty of Geographical Science at Beijing Normal University. She received the IEEE MIKIO Takagi Student Prize (best student paper award) at the International Geoscience and Remote Sensing Symposium (IGARSS) in 2016. She currently serves as an associate editor for IEEE Transactions on Geoscience and Remote Sensing. Her research interests encompass remote sensing image analysis and computer vision. By integrating traditional remote sensing theory with artificial intelligence techniques, she has achieved breakthroughs in unsupervised and unregistered image fusion, as well as open-set land cover classification. These advancements have significantly enhanced the accuracy and efficiency of urban destruction detection and large-scale mapping.


    Bioinspired AI and IoT: Crafting Intelligent Ecosystems from Homes to Forests

    Liyanage C De Silva, PhD & Professor,
    Dean, School of Digital Science (SDS), Universiti Brunei Darussalam (UBD), Brunei Darussalam
    Email: liyanage.silva@ubd.edu.bn

    Abstract: We are at a unique juncture where bioinspiration, Artificial Intelligence (AI), and the Internet of Things (IoT) converge to offer transformative solutions for pressing environmental and societal challenges. By integrating bioinspired algorithms with AI-driven sensor technology, we achieve a level of synergy that allows for intelligent interpretation of multifaceted environmental data. This approach, deeply rooted in biomimicry, elevates our systems from mere data collectors to dynamic problem solvers. It offers predictive analytics and automated responses in critical areas such as pollution mitigation, hazardous chemical detection, and disaster prevention. In this presentation we will navigate through this bioinspired, AI-augmented world of smart homes. We'll explore AI-driven, bioinspired smart homes equipped with computer vision and audio recognition algorithms, focusing on their specialized applications in eldercare, childcare, and energy efficiency. A dynamic landscape where bioinspired AI techniques not only enhance current IoT functionalities but also pave the way for innovative research domains, particularly in energy optimization and real-time video analytics. Extending this bioinspired AI-IoT framework to ecological conservation, we introduce the groundbreaking concept of the "Internet of Trees." Here, AI-powered, bioinspired sensors autonomously monitor forest health, predict potential natural calamities, and interact with wildlife in a manner mimicking natural processes. This fusion aims to create a real-time "Jungle- book, " revolutionizing our engagement and understanding of natural environments. Through the seamless integration of bio inspiration, AI, and IoT, we're not merely monitoring the world around us; we're intelligently and sustainably interacting with it. Join us as we unpack the boundless potential these converging, bioinspired technologies hold for a more sustainable and intelligent future.

    Bio-Sketch: Professor Liyanage C De Silva received BSc Eng(Hons) degree from the University of Moratuwa Sri Lanka in 1985, MPhil degree from The Open University of Sri Lanka in 1989, MEng and PhD degrees from the University of Tokyo, Japan in 1992 and 1995 respectively. He was with the University of Tokyo, Japan, from 1989 to 1995. From April 1995 to March 1997 he has pursued his postdoctoral research as a researcher at ATR (Advanced Telecommunication Research) Laboratories, Kyoto, Japan. In March 1997 he has joined The National University of Singapore as a Lecturer where he was an Assistant Professor till June 2003. He was with the Massey University, New Zealand from 2003 to 2007. Currently he is a Professor of Engineering and former Dean, Faculty of Integrated Technologies (FIT) and the Dean, School of Digital Science (SDS) at the Universiti Brunei Darussalam (UBD).Liyanage’s current research interests are Internet of Things (IoT) Neural Network Applications, Image and Speech Signal Processing (in particular multi modal emotion recognition and speech emotion analysis), Digital Communication (CDMA, OFDMA etc.), Information theory (source coding), Pattern recognition and understanding (biometric identification), Multimedia signal processing, and Smart Sensors (Smart environments for security, eldercare and energy efficiency). Liyanage has published over 180 technical papers in these areas in international conferences, journals and Japanese national conventions and jointly holds three US, one Brunei and one Japanese national patent. The Japanese national patent was successfully sold to Sony Corporation Japan for commercial utilization. Liyanage’s works have been cited as one of the pioneering works in the bimodal (audio and video signal based) emotion recognition by many researchers. His papers so far have been cited by more than 5200 times (according to scholar.google.com) with an h-index of 30. Prof Liyanage C De Silva (LC De Silva) - Google Scholar