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MSc in Sports Technology and Management: Study Tour in Mainland China

The MSc in Sports Technology and Management students continued their global learning journey in Mainland China. Alongside their study tour in Spain, our students also completed an enriching study trip to Mainland China to explore the region’s rapidly evolving sports ecosystem. The itinerary was packed with high-level visits to top sports brands, research institutions, world-leading high-tech companies, and state-of-the-art training facilities – giving students hands-on insights into sports engineering that enhances elite athlete development. They also had exclusive visit to Beijing Sports University and Shanghai University of Sport, premier first-class institutions affiliated with the General Administration of Sport of China. Students got an in-depth, behind-the-scenes look at how advanced biomechanics and data-driven sports engineering come together to nurture world-class athletes. 🏋️‍♂️📊 This trip provided an invaluable bridge between academic theory and real-world industry practices, leaving our students inspired to drive future innovation in sports tech. Learn more about our MSc in Sports Technology and Management: here

MSc in Sports Technology and Management: Study Tour in Spain

Our MSc in Sports Technology and Management students embarked on an immersive study tour across Spain in June 2026, exploring the heart of Europe’s vibrant sports industry. Designed to bridge classroom theory with real-world applications, the trip gave students an insider look into sports engineering, elite athletic performance, and global sports management. Through hands-on workshops and direct exchanges with leading industry experts, participants gained valuable insights into the latest global advancements shaping the market. A major highlight of the journey was attending the Barcelona-Catalunya Grand Prix 2026. Experiencing a world-class Formula 1 race firsthand allowed students to analyse elite-level event operations, high-performance data analytics, and fan engagement strategies in real time. 🏁🏎️ The trip was a truly transformative experience. Beyond broadening their global perspectives, students returned deeply inspired – equipped with newfound knowledge and industry connections that will directly enrich their ongoing academic journey and future careers in sports technology. Learn more about our MSc in Sports Technology and Management: here

Distinguished Lecture "Advances in Computational Sports Biomechanics" by Professor Justin FERNANDEZ

Distinguished Lecture "Advances in Computational Sports Biomechanics" by Professor Justin FERNANDEZ   Date: 31 August 2026 (Monday) Time: 2:00pm-3:00pm (HK Time) Speaker: Professor Justin FERNANDEZ   Professor   Department of Engineering Science and Biomedical Engineering   Auckland Bioengineering Institute   University of Auckland, New Zealand Venue: Room HJ303, The Hong Kong Polytechnic University (face-to-face only) Registration: https://polyu.hk/aTOMw   About the speaker Prof. Justin FERNANDEZ is a Professor in the Department of Engineering Science and Biomedical Engineering, and the Auckland Bioengineering Institute, University of Auckland. He graduated with a Bachelor of Engineering (Honours first class) in Engineering Science (1999) and a PhD in Bioengineering from the University of Auckland (2004). Between 2005 and 2010 he completed an ARC postdoctoral fellowship at Melbourne University in Mechanical Engineering and was a research scientist at the CSIRO. From 2011 to 2013 he was an Aotearoa Fellow funded by the Robertson Foundation in the Auckland Bioengineering Institute. He is a Principal Investigator in the musculoskeletal modelling group where he leads research into computational biomechanics, orthopaedics, sports science, brain mechanics, and ADHD. His work integrates biomedical imaging, gait analysis, wearable sensors, large population data, statistical models, and computational mechanics. His research strongly focuses on clinician engagement. Distinctions/Honours: Vice Chancellors prize for best doctoral thesis (2004, University of Auckland) Aotearoa Bioengineering Fellowship (2011-2013, University of Auckland) ARC Postdoctoral Fellowship (2006-2008, University of Melbourne) Winner of the 2017 Healthtech Care Award (MedTech CoRE) - best translational project Editorial board of Medical Engineering and Physics Adjunct Professor at Ningbo University, Institute of Health, China Co-founder of Formus Labs (www.formuslabs.com)  

Distinguished Lecture "Intelligent Robotics for Effective Stroke Rehabilitation Treatment: Challenges and Opportunities" by Professor Shane Sheng Quan XIE

Distinguished Lecture "Intelligent Robotics for Effective Stroke Rehabilitation Treatment: Challenges and Opportunities" by Professor Shane Sheng Quan XIE   Date: 21 August 2026 (Friday) Time: 11:45am-12:45pm (HK Time) Speaker: Professor Shane Sheng Quan XIE   Chair of Robotics and Autonomous Systems,   Director of the Rehabilitation Robotics Lab,   School of Electronic and Electrical Engineering,   University of Leeds, UK Venue: Room Y301, The Hong Kong Polytechnic University Zoom: https://polyu.hk/UfRFq ((Meeting ID: 842 6154 8170  ;  Passcode: 762518)   Abstract Stroke and neurological diseases have significant impact on our society, robotic technologies have shown potential for delivering effective care and presented many opportunities for the healthcare industry. The talk will cover the recent development of robotics for stroke rehabilitation, the research gaps and the need for new technologies in neuroscience, robotics and artificial intelligence. The talk will introduce an EPSRC-funded project on intelligent reconfigurable exoskeletons tailored to meet patients’ needs, deliver effective diagnosis and personalised treatment, and monitored remotely by rehabilitation therapists. Examples of some of the current ongoing research work at the Leeds Centre for Assistive/Rehabilitation Robotics will be presented including pneumatic Peano muscle, DEA, soft exoskeleton, bilaterial robot, neuromuscular and brain computer interfaces. The focus is on enabling technologies for those whose strength and coordination have been affected by amputation, stroke, spinal cord injury, cerebral palsy and ageing.   About the speaker Prof. Shane (Sheng Q) Xie, Ph.D., FRSNZ, FEngNZ, FIEEE, FASME, FIMechE and FAAIA, is the Chair of Robotics and Autonomous Systems and Director of the Rehabilitation Robotics Lab at the University of Leeds, and he was the Director of the Rehabilitation and Medical Robotics Centre at the University of Auckland, New Zealand (NZ, 2002-2016). He has 30 years of research experience in healthcare robotics and exoskeletons. He has published 500 refereed papers and 8 books in rehabilitation exoskeleton design and control, neuromuscular modelling, and advanced human-robot interaction. He has supervised 15 postdocs, 100 PhDs and 80 MEs in his team with funding of £30M from five countries since 2003. His team has invented three award-winning rehabilitation exoskeletons. He is an expert in control of exoskeletons, i.e. impedance control, adaptive control, sliding mode control, and iterative learning control strategies. He has received many distinguished awards including the New Zealand Science Challenge Award, the David Bensted Fellowship Award, and the AMP Invention Award. He is an elected Fellow of Royal Society of New Zealand, Fellow of Engineering New Zealand, Fellow of IEEE, ASME, IMechE and AAIA. He was the Technical Editor for IEEE/ASME Transaction on Mechatronics, Associate Editor for Mechatronics Elservier and Editorial member of many top journals in Mechatronics and Robotics.  

Congratulations to Ir Prof. Yongping ZHENG on winning two awards at the PolyU Patents Achievement Award 2025

Congratulations to Ir Prof. Yongping ZHENG on receiving two awards at the PolyU Patents Achievement Award 2025 (Category of Award: Inventor): 🏆 Top Patents Filing Award 🏆 Outstanding IP Commercialisation Award The PolyU Patents Achievement Award (PPAA) is a prestigious university-level accolade that recognises academic departments and faculty members for their exceptional commitment to knowledge transfer and intellectual property creation. Prof. ZHENG’s remarkable contributions to innovation and technology commercialisation are truly inspiring, paving the way for impactful solutions that benefit society. His well-deserved recognition reflects both his visionary research and BME’s dedication to translating academic achievements into real-world applications.

Ir Prof. Ming Zhang Invited as Plenary Speaker at the 10th World Congress on Biomechanics (WCB 2026)

Ir Prof. Ming ZHANG, Head of Department of Biomedical Engineering, Director of Research Institute for Sports Science and Technology and Chair Professor of Biomechanics, was invited as a Plenary Speaker at the 10th World Congress on Biomechanics (WCB 2026), held on 11-15 July in Vancouver, Canada. Prof. ZHANG's presentation, titled "Foot and Ankle Biomechanics for Footwear Design," highlighted cutting-edge research in the field. Held only once every four years, the WCB is the premier international event in its field. It attracted more than 3,000 delegates from across the globe, providing an opportunity for the exchange of information about all aspects of modern biomechanics. This invitation reflects Prof. ZHANG’s outstanding contributions and international leadership in foot and footwear biomechanics research.

Hollow Silica Nanostructures with Ultrasound Technology Offer a Less Invasive and Precise Treatment Alternative for Parkinson's Disease Patients

Parkinson’s disease (PD) has long been regarded as an irreversible condition among older adults. Although existing treatments may manage symptoms, they are often invasive or limited in precision and effectiveness. In a recently published study on Nature Communications, Prof. Lei SUN, Professor of the Department of Biomedical Engineering at PolyU, and his research team addressed the need for neuromodulation approaches that are both precise and less invasive by combining low-intensity ultrasound with engineered hollow silica nanostructures (HSN) to achieve chronic, localised brain stimulation in mice, as well as therapeutic benefit in PD models. This study provides a compelling proof of concept for chronic, localised and minimally invasive neuromodulation using ultrasound and HSN. This research offers a more precise, safer and durable solution that brings new hope to patients with limited treatment options. Find more details in the full article on Innovation Digest: https://polyu.hk/aXfeL

2026 PolyU BME Alumni Connect: Happy Hour

🍷 What a night! Cheers to the PolyU BME family! 🥂 On June 26, our BME community gathered for the 2026 PolyU BME Alumni Connect: Happy Hour — an evening filled with inspiration and joy!   Here are the highlights of the night:   ✨ Department Updates: Prof. Ming ZHANG, Head of Department of Biomedical Engineering, shared our latest milestones and future vision.   🏆 Celebration of Excellence: We officially celebrated Prof. Weibao QIU, the recipient of the 2026 Outstanding Alumni Award of PolyU BME (the award announcement post is here).   💡 Industry Insights: A huge thank you to our guest speakers, Mr Benjamin CHAN, Founder and Managing Director, MediConcepts Limited, and our stellar alumna Ms Yung KWOK (Capital Listing Acceleration Manager, Medtronic; graduate of the Class of 2012), for sharing their invaluable industry wisdom.   🍇 Sip & Socialize: The night wrapped up with an exclusive wine-tasting workshop hosted by professional sommelier Mr Micky CHAN.   It was wonderful seeing our alumni reconnecting. Thank you to everyone who joined us!

Prof. Puxiang LAI’s research on “Physics-Guided Dataset Homogeneity Unlocks Universal Deep Learning Generalization in Scattering Media Imaging” published in Advanced Photonics

Research paper titled “Physics-Guided Dataset Homogeneity Unlocks Universal Deep Learning Generalization in Scattering Media Imaging”, with Professor Puxiang LAI as the co-correspondence author, was recently published in Advanced Photonics (IF=19.5; ranked 7th out of 129 journals in Optics). This work introduces a physics-guided dataset homogeneity strategy and demonstrates that enforcing spatial uniformity — ensuring all spatial modes in the region of interest are equally and sufficiently sampled — effectively aligns the learned weights with the physical transmission matrix. This approach ensures the network simulates the underlying physical laws rather than merely memorizing dataset-specific statistical biases.   Xuyu Zhang, Haofan Huang, Dawei Zhang, Songlin Zhuang, Shensheng Han, Puxiang Lai*, and Honglin Liu*, "Physics-Guided Dataset Homogeneity Unlocks Universal Deep Learning Generalization in Scattering Media Imaging", Advanced Photonics 8 (3): 036015 (2026). doi: 10.1117/1.AP.8.3.036015   Abstract Deep learning (DL) has revolutionized imaging through scattering media, yet its widespread adoption is hindered by limited generalization, where models trained on specific datasets fail to perform reliably in unseen scenarios. Conventional wisdom attributes this limitation to feature-prior mismatches, but we identify a more root cause: a fundamental mismatch between the learned neural mapping and the system’s true physical inverse operator (T -1), driven principally by inhomogeneous spatial-intensity distributions in conventional training data. To overcome this, we introduce a physics-guided dataset homogeneity strategy. We demonstrate that enforcing spatial uniformity — ensuring all spatial modes in the region of interest are equally and sufficiently sampled — effectively aligns the learned weights with the physical transmission matrix. This approach ensures the network simulates the underlying physical laws rather than merely memorizing dataset-specific statistical biases. Specifically, by optimizing training datasets, we achieve unprecedented cross-dataset generalization: networks trained on simple digits successfully reconstruct complex face images. This physics-guided framework not only overcomes generalization barriers in scattering imaging but also establishes a universal principle for designing robust DL architectures. The conceptual repositioning of DL, from pure data-fitting to physics-simulating, is a big step forward for its reliable deployments in real-world imaging applications.   About Advanced Photonics Advanced Photonics is a highly selective, open-access, international journal that publishes innovative research in all areas of optics and photonics, including fundamental and applied research. The journal publishes top-quality original papers, letters, and review articles, reflecting significant advances and breakthroughs in theoretical and experimental research and novel applications with considerable potential.

Prof. Puxiang LAI’s research on “From Disorder to Design: Physical Mechanisms Governing Generalization and Hallucination in Deep Learning for Imaging Through Scattering Media” published in Nature Communications

Research paper titled “From Disorder to Design: Physical Mechanisms Governing Generalization and Hallucination in Deep Learning for Imaging Through Scattering Media”, with Professor Puxiang LAI as the co-correspondence author, was recently published in Nature Communications (IF=18.1; ranked 8th out of 140 SCI journals in Multidisciplinary Sciences). This work leverages a physics-guided framework based on scattering media and developed a model system where controlled variations in light transmission matrices (T) isolates the long-standing challenges in the field, unravelling the mechanistic interplay between generalization limits and hallucination origins.   Xuyu Zhang, Tianting Zhong, Haofan Huang] Dawei Zhang, Songlin Zhuang, Shensheng Han, Puxiang Lai*, and Honglin Liu*, "From Disorder to Design: Physical Mechanisms Governing Generalization and Hallucination in Deep Learning for Imaging Through Scattering Media", Nature Communications 17: 5616 (2026). doi: 10.1038/s41467-026-72304-z   Abstract Deep learning has revolutionized computational imaging, yet its real-world deployment remains constrained by two critical challenges: poor generalization under dynamic conditions and the emergence of hallucinatory artifacts. By leveraging a physics-guided framework based on scattering media, a model system where controlled variations in light transmission matrices (T) isolates these challenges, we unravel the mechanistic interplay between generalization limits and hallucination origins. We demonstrate that a network’s generalization capacity is fundamentally bounded by its ability to accommodate distinct inverse mappings (T -1), while hallucinations arise when this capacity is exceeded, resulting in unconstrained, non-physical predictions. We also identify residual ballistic light, if not negligible, as a stabilizing anchor, enabling robust predictions under scattering variability. Integrating experimental validation with wave-optics simulations, we establish a universal framework that links these phenomena, showing that strategic training on diverse physical mappings enhances generalization while suppressing hallucinations. This work bridges physics-driven interpretability with AI design, offering actionable strategies to develop reliable models for applications ranging from medical imaging through biological tissues to autonomous navigation in scattering environments.   About Nature Communications Nature Communications is an open access, multidisciplinary journal dedicated to publishing high-quality research in all areas of the biological, health, physical, chemical, Earth, social, mathematical, applied, and engineering sciences.