BIOGRAPHY
Dario Farina is Full Professor and Chair of Neurorehabilitation Engineering at Imperial College London (since 2016), internationally recognised for pioneering neural interfacing, biomedical signal processing, and neurorehabilitation engineering. Previously, he directed the Institute for Neurorehabilitation Systems at the University Medical Center Göttingen (2010–2016) and was a Full Professor at Aalborg University (2008–2010), following early appointments in Italy and France. He has authored more than 700 journal papers (including 100+ in IEEE Transactions titles), holds an h-index of 145 with more than 74,000 citations, and has published in leading venues such as Lancet, Nature Biomedical Engineering, Science Robotics, Nature Computational Science. He is the Editor-in-Chief of the Journal of Electromyography and Kinesiology (since >10 years), Associate Editor of Science Advances, Senior Editor for IEEE Transactions on Biomedical Engineering (editor since >20 years), Associate Editor of IEEE Reviews in Biomedical Engineering, among other journals, and has been elected Fellow of IET (2026), IEEE (2018), EAMBES (2016), ISEK (2012). He holds a Honorary Doctorate degree in Medicine from Aalborg University, Denmark, has been elected Foreign Member of the Danish Royal Academy of Sciences and Letters, and has been the recipient of the IEEE Engineering in Medicine and Biology Society Early Career Achievement Award, the Royal Society Wolfson Research Merit Award, IEEE EMBS Technical Achievement Award, and the ISEK Basmajian award, among other recognitions. As Principal Investigator he has secured major competitive funding, including 6 ERC Grants (Advanced, Synergy, and 4 Proof of Concept). He has a strong innovation record as scientific advisor and shareholder in CTRLLabs (acquired by Meta), ongoing advisor to Meta, founder of Neubound and AxonCtrl, and holder of patents licensed to Otto Bock and Cala Health. A dedicated mentor and educator, he has supervised more than 50 PhD graduates and mentored over 70 postdoctoral researchers.
PRESENTATION TITLE
Minimally invasive, large scale neural interfaces for assistance and augmentation
ABSTRACT
Neural interfaces promise to restore and enhance human motor function, but current approaches face a tradeoff: invasive brain and spinal implants offer high signal quality at the cost of surgical risk, while wearable systems are safe yet provide limited access to the underlying neural code. Alpha motor neurons convert synaptic input into the neural drive to muscles and provide a unique, practical interface with the output of the spinal cord. Their spiking activity can be identified from muscle electrical signals using minimally invasive, thin-film intramuscular sensors, enabling study of individual neurons during natural behaviour without brain implants. This talk will present minimally invasive, largescale motor neuron interfacing technologies and AIbased decoding of neural activity. I will illustrate how these approaches advance our understanding of movement neuroscience and enable neural interfaces for assistive technologies in people with motor impairments, as well as future human augmentation.
BIOGRAPHY
Prof. Hongliang Ren is a full professor at the Chinese University of Hong Kong (CUHK) in the Department of Electronic Engineering, where he received his Ph.D. in 2008. He has served as an Associate Editor for IEEE Transactions on Automation Science & Engineering (T-ASE) and Medical & Biological Engineering & Computing (MBEC). He has navigated his academic journey through Chinese University of Hong Kong, Johns Hopkins University, Children's Hospital Boston, Harvard Medical School, Children's National Medical Center, United States, and National University of Singapore (NUS).
His areas of interest include biorobotics, intelligent control, medical mechatronics, soft continuum robots, soft sensors, and multisensory learning in medical robotics. He is the recipient of the National Science Fund for Distinguished Young Scholars (Scheme A), CUHK Young Researcher Award, NUS Young Investigator Award and Engineering Young Researcher Award, IAMBE Early Career Award 2018, Interstellar Early Career Investigator Award 2018, ICBHI Young Investigator Award 2019, and Health Longevity Catalyst Award 2022 by NAM & RGC, Best Paper Awards in IEEE-ROBIO (2019 & 2013), IEEE-RCAR2016, IEEE-CCECE2015, IEEE-Cyber2014 among 30+ other prestigious awards. He has been constantly listed among the world's top 2% of the most-cited scientists by Stanford University in the career-long category. He has published over 240 papers, accumulating more than 23,000 citations and achieving an H-index of 77.
He frequently served as an expert reviewer/judge for international funding agencies (60+ proposal reviews) of 10+ countries/regions.
PRESENTATION TITLE
Endoscopic Multisensory Navigation With Soft Flexible Robotics
ABSTRACT
Minimally Invasive Surgeries (MIS) emerging in modern medical treatment have brought new opportunities and challenges for procedure-specific surgical motion generation and the associated motion understanding, which are the foundation of intelligent robotic manipulation and guiding interventions. Image-guided robotic surgery is expected to increase the precision, flexibility, and repeatability of surgical procedures but poses challenges for system development. This talk will highlight our recent developments in dexterous robotic motion generation with motion perception towards intelligent image-guided minimally invasive procedures. The procedure-specific telerobotic surgical systems can assist surgeons in performing dexterous manipulations using continuum motion generation mechanisms with variable stiffness and context awareness.
BIOGRAPHY
Hideyuki Hasegawa received the B.E., M.S., and Ph.D. degrees in engineering from Tohoku University, Japan. He is currently a Professor with the Faculty of Engineering, University of Toyama, where he leads the Laboratory of Medical Information Sensing. Prior to joining the University of Toyama, he served as Assistant Professor and Associate Professor at Tohoku University. His research interests include ultrasound beamforming, quantitative blood flow imaging, vector flow imaging, arterial biomechanics, ultrafast ultrasound imaging, speed-of-sound estimation, scanning acoustic microscopy, and medical signal processing. He has made significant contributions to ultrasound-based quantitative assessment of cardiovascular function, particularly through the development of novel methods for blood flow velocity estimation and high-frame-rate imaging.
Prof. Hasegawa has actively contributed to the international ultrasound community through various leadership roles. He served as Associate Editor of the IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, Chair of the Technical Program Committee of the World Federation for Ultrasound in Medicine and Biology (WFUMB) 2025, and Website Editor of the Asian Federation of Societies for Ultrasound in Medicine and Biology (AFSUMB), and currently serves as a member of the Technical Program Committee of the IEEE International Ultrasonics Symposium (IUS), including Beamforming Track Lead (2020–2022), Councilor of the AFSUMB, Vice President of the Japan Society of Ultrasonics in Medicine (JSUM), and Chair of the International Exchange Committee of the JSUM.
PRESENTATION TITLE
Ultrasound Quantitative Blood Flow Imaging
ABSTRACT
Ultrasound imaging plays an essential role in the diagnosis and management of cardiovascular diseases because of its real-time capability, portability, safety, and cost-effectiveness. While conventional Doppler ultrasound provides qualitative or one-dimensional measurements of blood flow, increasing clinical demands have motivated the development of quantitative imaging techniques capable of accurately assessing complex hemodynamics.
This lecture reviews recent advances in ultrasound quantitative blood flow imaging, with a particular focus on methodologies developed to improve the accuracy and robustness of flow measurement. The talk first introduces the fundamental principles of Doppler-based flow estimation and discusses their inherent limitations, including angle dependence, aliasing, clutter interference, and restricted temporal and spatial resolution.
Subsequently, recent developments in quantitative blood flow imaging are presented, including high-frame-rate imaging enabled by plane-wave transmission, vector flow imaging, advanced clutter filtering techniques, and signal processing methods for accurate velocity estimation. These technologies enable more reliable visualization and quantification of complex flow patterns associated with vascular diseases.
BIOGRAPHY
Prof. Ir. Dr. Mohammad Faizal Ahmad Fauzi, PEng, CEng, SMIEEE, MIEM, is a distinguished Malaysian academic, engineer, and researcher, currently serving as Professor and Dean of the School of Digital Health (SDH) at KPJ Healthcare University (KPJU) and Director of the KPJ Centre for Digital Health (CDH). Previously he was with Multimedia University (MMU) for more than 25 years. Renowned for his pioneering contributions to biomedical informatics, digital pathology, machine learning and artificial intelligence, he has spent over two decades driving innovation at the intersection of engineering, computer science and healthcare. Prof. Faizal earned his BEng in Electrical and Electronic Engineering degree from Imperial College London and PhD in Electronic and Computer Science from the University of Southampton, UK. His leadership in AI-driven healthcare research, particularly in cancer diagnostics, medical imaging, and precision medicine, has been recognised through prestigious awards including Malaysia's Top Research Scientist (ASM, 2024), the Fulbright-MCMC Award (USA, 2017), and multiple honours from the Institute of Electrical and Electronics Engineers (IEEE) and the Islamic Development Bank (IsDB). A Chartered Engineer (UK) and Professional Engineer (Malaysia), he has published widely, holds several patents, and has delivered numerous international keynote lectures. As an academic leader and mentor, he continues to champion the integration of digital health technologies, clinical AI, and research excellence to strengthen Malaysia's position in the global digital health ecosystem.
PRESENTATION TITLE
Towards Precision Medicine: Artificial Intelligence for Cancer Treatment Recommendation
ABSTRACT
Digital pathology refers to the acquisition, management, analysis, and interpretation of pathology information within a digital environment, enabling more efficient, accurate, and cost-effective approaches to the diagnosis, prognosis, and prediction of cancer and other diseases. The advent of whole-slide imaging (WSI) has transformed diagnostic pathology by enabling the digitisation of histopathological slides and facilitating the integration of advanced computational methods, including artificial intelligence (AI) and machine learning (ML). These developments have positioned digital pathology as a rapidly evolving and highly promising field in modern clinical practice. In this talk, we will present our work on the development of an AI-based computer-aided diagnosis system for automated assessment and scoring of tumor biomarkers from digital pathology slides to support breast cancer treatment recommendations. The system is designed to enhance the efficiency, consistency, and objectivity of biomarker assessment and prognostic reporting. By reducing diagnostic errors and inaccuracies, minimising sampling bias and inter-observer variability, enabling faster and more consistent reporting, and reducing the workload of pathologists, AI-assisted digital pathology has the potential to strengthen clinical decision-making and improve the overall quality of breast cancer management.