جست‌وجوی استاد و پژوهشگر

Satyvir Singhمشاهده پروفایل

پژوهشگر ارشد

• High-Order Discontinuous Galerkin Method
• Computational Fluid Dynamics
• Hydrodynamic Instability
• +۲ مورد دیگر

Dr. Satyvir Singh is a Research Fellow at RWTH Aachen University (since 2022). He holds a Ph.D. in Mechanical and Aerospace Engineering from Gyeongsang National University, South Korea, and prior academic roles include Postdoctoral Fellowships at Nanyang Technological University (Singapore) and teaching positions in India. His research focuses on high-order numerical methods for fluid dynamics, particularly discontinuous Galerkin methods applied to hydrodynamic instabilities, computational fluid dynamics, and gas kinetic theory. Education: Ph.D., Mechanical and Aerospace Engineering, Gyeongsang National University, South Korea (2013-2016) M.Tech., Industrial Mathematics and Scientific Computing, IIT Madras, India (2009-2011) M.Sc., Mathematics, Chaudhary Charan Singh University, India Research Interests: High-Order Discontinuous Galerkin Method Computational Fluid Dynamics Hydrodynamic Instability (Richtmyer-Meshkov, Rayleigh-Taylor) Multispecies Flows and Gas Kinetic Theory Scientific Contributions include over 30 peer-reviewed articles in journals like Journal of Computational Physics , Physics of Fluids , and SCIENCE CHINA Physics , focusing on shock-driven instabilities, numerical methods for rarefied gases, and reaction-diffusion systems. Awards and Fellowships: Brain Korea 21+ Scholarship, South Korea (Ph.D.) GATE HMRD Scholarship, India (M.Tech.) Qualified JRF/NET (Mathematical Sciences), AIR 38 (2011) Labs and Affiliations: Part of the The Lab at RWTH Aachen University, contributing to interdisciplinary fluid dynamics research.

• Nanotheranostics
• Quantitative Imaging
• MRI
• +۲ مورد دیگر

Holden H Wu is a Professor in the Department of Radiological Sciences at the University of California Los Angeles (UCLA) School of Medicine. His research focuses on advanced medical imaging techniques, particularly in quantitative MRI, artificial intelligence applications, and image-guided interventions. He leads the UCLA MRRL Wu Lab and has established himself as a leading researcher in free-breathing MRI techniques for body composition analysis and disease quantification. Dr. Wu's research interests span nanotheranostics, quantitative imaging, MRI technology development, artificial intelligence applications in medical imaging, and image-guided interventions. His work particularly emphasizes developing motion-robust techniques for abdominal and pediatric imaging, with applications in liver fat quantification, body composition analysis, and prostate cancer imaging. He has pioneered several free-breathing MRI techniques that have eliminated the need for breath-holding in patients, significantly improving clinical applicability, especially for pediatric populations and patients with limited breath-holding capacity. His recent publications demonstrate a strong focus on integrating deep learning with physics-based modeling to improve quantitative MRI techniques. Major trends in his research include the development of self-gated radial MRI techniques, motion-compensated imaging methods, and AI-powered analysis tools for medical imaging data. His work bridges engineering innovation with clinical applications, particularly in liver disease, metabolic disorders, and oncology. New Technologies for Real-Time MRI-Guided Robotic-Assisted Abdominal Interventions (NIH R01EB031934, 2022-2026) - Principal Investigator Quantitative MRI and Deep Learning Technologies for Classification of NAFLD (NIH U01EB031894, 2022-2027) - Principal Investigator Quantifying Body Composition and Liver Disease in Children using Free-Breathing MRI and MRE (NIH R01DK124417, 2020-2024) - Principal Investigator Integrating Quantitative MRI and Artificial Intelligence to Improve Prostate Cancer Classification (NIH R01CA248506, 2020-2025) - Co-Principal Investigator Dr. Wu has mentored numerous students and researchers through his active laboratory, focusing on training the next generation of biomedical imaging scientists. His research group, the UCLA MRRL Wu Lab, develops innovative imaging technologies with direct clinical translation potential. Current projects include developing real-time MRI-guided robotic interventions, advanced quantitative techniques for liver fat and fibrosis assessment, and AI-powered prostate cancer detection methods.

• Applied Mathematics
• Geology
• Resources Engineering and Extractive Metallurgy
• +۳ مورد دیگر

James Percival is a Senior Teaching Fellow in the Department of Earth Science & Engineering at Imperial College London, part of the Faculty of Engineering. His research focuses on computational methods for fluid dynamics, environmental engineering, and porous media flow. He is affiliated with the Applied Modelling and Computation Group and the Novel Reservoir Modelling and Simulation (NORMS) initiative. His work emphasizes numerical simulations using advanced techniques like discontinuous Galerkin methods and adaptive unstructured meshes. Key research areas include hydro-morphodynamics, multiphase flow modeling, and reservoir engineering. His publications highlight contributions to fluid dynamics, atmospheric modeling (e.g., ATHAM-Fluidity), and environmental applications such as pipeline scour analysis. Percival’s methodologies prioritize high-resolution simulations and mesh optimization for complex geophysical and industrial challenges. His articles reflect a trend toward integrating computational efficiency with accuracy in modeling phenomena like viscous fingering, interfacial flows, and extreme weather events. Despite significant contributions, no awards or grants are explicitly noted in the provided texts.

Georgios Fourtakasمشاهده پروفایل

مدرس

• Meshless Methods
• Computational Fluid Dynamics (CFD)
• Smoothed Particle Hydrodynamics (SPH)
• +۵ مورد دیگر

Dr. Georgios Fourtakas is a Lecturer in Civil Engineering at the University of Manchester’s School of Mechanical Aerospace and Civil Engineering. He specializes in meshless methods, computational fluid dynamics (CFD), and smoothed particle hydrodynamics (SPH). His research focuses on advancing SPH formulations for complex fluid-structure interactions, multiphase flows, and high-performance computing (HPC) applications, particularly through the DualSPHysics open-source framework. He holds a PhD from the University of Manchester (2014) and prior industry experience in aeronautical engineering and thermal fluid mechanics. Education: BEng in Aeronautical Engineering, University of Salford MSc in Aeronautical Engineering, Cranfield University MSc in Thermal Power and Fluid Mechanics (Academic Return) PhD in SPH Applications (University of Manchester) Key Research Areas: SPH formulations (Lagrangian, Eulerian-Lagrangian, ALE) GPU/CUDA acceleration for HPC Boundary condition development Non-Newtonian and sediment flows Fluid-structure interaction (e.g., heart valves) His work contributes to UN Sustainable Development Goals through applications in coastal engineering, cardiovascular systems, and nuclear decommissioning. He has reviewed for journals like Advances in Water Resources and Journal of Hydroinformatics . Recent articles highlight innovations in divergence cleaning for SPH, GPU-accelerated thrombus modeling, and poroelasticity simulations. His DualSPHysics project is widely adopted for real-world engineering problems.

Agostino Marinelliمشاهده پروفایل

استادیار

• Photon Science
• Particle Physics
• Astrophysics
• +۳ مورد دیگر

Agostino Marinelli is Assistant Professor of Photon Science and Particle Physics and Astrophysics at SLAC National Accelerator Laboratory, Stanford University. He leads the free-electron laser physics department and co-directs the FEL R&D program, focusing on X-ray free-electron lasers and ultrafast light sources. His research integrates accelerator physics, photon science, and quantum optics to develop advanced light sources. Recent publications demonstrate strong thematic coherence across attosecond science, beam manipulation, and instrumentation innovation. His articles show consistent exploration of pulse control mechanisms, beam dynamics, and novel diagnostic methods, with applications spanning quantum dynamics, materials science, and instrumentation development.

Chris Nonhofمشاهده پروفایل

دانشیار

• Language and Identity
• Qualitative Research Methodology
• Diversity in Education
• +۳ مورد دیگر

Dr. Chris Nonhof serves as an Associate Professor of Education and English and Chair of the English Department at Northwestern College. He has extensive experience in secondary education, having taught English, theatre, and argumentative writing in private and public schools in Florida and Wisconsin before joining Northwestern College. He holds a Ph.D. in Language and Literacy from Cardinal Stritch University, an M.Ed. in Instructional Technology from the same institution, and a B.A. in English Literature, Theatre Arts, and Secondary Education from Dordt University. His research focuses on language and identity, qualitative research methodologies (particularly narrative, ethnography, and case studies), diversity in education, and culturally responsive pedagogy. Recent work explores AI's impact on English education and strategies for student-centered curricula that prioritize individual narratives over traditional canons. Nonhof has presented at numerous conferences, including the Iowa Council of Teachers of English and the International Network for Christian Higher Education. His publications span topics like discourse analysis in Liberian orphanage schools and code-switching in academic settings. He has received awards such as the Northwestern Teaching Excellence Award (2022) and the Blekkink Endowed Chair of Education (2019-2024). He maintains active membership in professional organizations including the National Council of Teachers of English and the Modern Language Association. His teaching philosophy emphasizes ethical pedagogy and bridging divides through communication, informed by his diverse roles as a teacher, theatre director, and tennis coach.

Xianyi Zengمشاهده پروفایل

استادیار

• Applied Mathematics
• Computational Mathematics
• Numerical Methods for Partial Differential Equations
• +۷ مورد دیگر

Xianyi Zeng is an Assistant Professor in the Department of Mathematics at Lehigh University. He holds a Ph.D. in Computational Mathematics from Stanford University (2012), an M.S. in Financial Mathematics from Stanford (2010), and a B.S. in Mathematics from Peking University (2006). His research focuses on numerical methods for partial differential equations, particularly hyperbolic conservation laws and fluid-structure interactions. He has developed innovative frameworks like the hybrid-variable (HV) discretization and contributed to the shifted boundary method (SBM) for compressible fluid dynamics. His work also bridges computational mechanics, mathematical biology (e.g., tumor growth modeling), and high-performance computing. Education: Ph.D., Computational Mathematics, Stanford University (2012) M.S., Financial Mathematics, Stanford University (2010) B.S., Mathematics, Peking University (2006) Research Interests include: Numerical methods for PDEs with complex geometries Hyperbolic conservation laws and shock hydrodynamics Mathematical modeling of tumor dynamics and virotherapy Reduced-order modeling and computational efficiency Recent work emphasizes high-order accuracy, stability, and scalability in simulations of multiphysics problems. His publications span journals like Journal of Computational Physics and Computer Methods in Applied Mechanics and Engineering . Teaching includes courses on differential equations at Lehigh, integrating analytical and numerical techniques. He has held faculty positions at UT El Paso (2016–2022) and conducted postdoctoral research at Duke University (2012–2016).

• VLSI Design
• Integrated Circuits
• Computer Architecture
• +۶ مورد دیگر

Xiaoyang Zeng is a Professor at Tsinghua University's School of Information Science and Technology, Institute of Microelectronics, with an extensive research portfolio in VLSI design, integrated circuits, and hardware acceleration systems. With over 429 publications spanning from 2005 to 2025, Professor Zeng maintains an exceptionally active research program, particularly evident in the high publication volume in recent years (45 papers in 2024 and 28 projected for 2025). His collaborative network includes prominent researchers such as Yibo Fan, Jun Han, Xu Cheng, and Xiaoyong Xue. Professor Zeng's research focuses on cutting-edge areas including Compute-in-Memory architectures, neuromorphic computing, low-power circuit design, and hardware acceleration for AI applications. His work bridges theoretical innovation with practical implementation, as evidenced by numerous publications in top-tier IEEE journals including the Journal of Solid-State Circuits, Transactions on Circuits and Systems, and Transactions on VLSI Systems. Recent work demonstrates particular strength in RRAM-based CIM accelerators, energy-efficient converters, and advanced signal processing techniques. The publication trends show a strategic evolution from traditional circuit design toward emerging computing paradigms, with increasing focus on AI hardware acceleration, neuromorphic systems, and energy-efficient computing solutions. His research group has developed innovative approaches to address challenges in memory-centric computing, analog circuit design, and hardware implementation of machine learning algorithms, with applications spanning consumer electronics, medical devices, and edge computing systems. Selected Scientific Awards: IEEE Journal of Solid-State Circuits Best Paper Award (2022) National Natural Science Award of China (Second Class, 2020) IEEE Asian Solid-State Circuits Conference Best Paper Award (2019) Professor Zeng has successfully advised numerous graduate students who have become active contributors in the field, with several now leading their own research projects. His research has been supported by multiple national-level grants from the National Natural Science Foundation of China and the Ministry of Science and Technology, focusing on next-generation computing architectures and advanced circuit design methodologies. The research group maintains strong industry connections with leading semiconductor companies for technology transfer and practical implementation of research outcomes.

• Ultrasound Imaging
• Medical Engineering
• Photoacoustic Imaging
• +۵ مورد دیگر

Professor Georg Schmitz is a distinguished academic in the field of medical engineering and ultrasound imaging at Ruhr-University Bochum, Germany. He holds the chair for medical engineering within the Faculty for Electrical Engineering and Information Technology and has served as Dean of this faculty from 2009 to 2012. Since 2014, he has been a member of the Senate of Ruhr-University Bochum. Prof. Schmitz received his Dipl.-Ing. degree in 1990 and Dr.-Ing. degree in 1995 in electrical engineering from Ruhr-Universität Bochum. Prior to his current position, he worked as a Principal Scientist at Philips Research Laboratories from 1995 to 2001 and served as Professor for Medical Engineering at the University of Applied Science Koblenz from 2001 to 2004. His research primarily focuses on ultrasound imaging, with current projects investigating ultrasound contrast media detection and characterization, novel beamforming and nonlinear reconstruction methods, and photoacoustic imaging. Prof. Schmitz is particularly known for his work in super-resolution ultrasound, ultrasound localization microscopy, and the application of deep learning techniques to medical ultrasound imaging. His recent publications demonstrate significant advancements in microvascular imaging, cancer diagnostics, and image reconstruction algorithms. Prof. Schmitz's research output shows a clear trajectory toward integrating artificial intelligence with traditional ultrasound techniques, with numerous publications focusing on deep learning applications for image enhancement, needle localization, and super-resolution imaging. His work bridges theoretical signal processing with practical clinical applications, particularly in oncology and microcirculation imaging. Senior member of the IEEE Member of the Acoustical Society of America (ASA) Member of the German Association of Electrical Engineers (VDE) Member of WFUMB, EFSUMB, and DEGUM Former associate editor of IEEE Transactions of Ultrasonics, Ferroelectrics, and Frequency Control (15+ years) Member of Editorial Advisory Board of Ultrasound in Medicine and Biology Prof. Schmitz has played significant leadership roles in the international ultrasound community, serving as vice chair (medical ultrasound) of the IEEE International Ultrasonics Symposium technical program committee from 2013-2015, and as technical program chair for the symposium in Washington, D.C. (2017) and Venice (2022). His extensive collaborations with researchers including S. Dencks, T. Lisson, and M. Fouad have resulted in numerous high-impact publications spanning ultrasound technology, medical diagnostics, and imaging algorithms. His laboratory focuses on advancing ultrasound localization microscopy techniques and developing novel approaches for clinical applications, particularly in cancer imaging and treatment monitoring. The team's work on microbubble technology, photoacoustic imaging, and AI-enhanced ultrasound demonstrates a comprehensive approach to solving complex medical imaging challenges.

• Mathematical Physics
• Fluid Dynamics
• Kinetic Theory
• +۳ مورد دیگر

Giorgio Martalò is a Researcher in the Department of Mathematical Physics at the University of Pavia. His work bridges mathematical physics, fluid dynamics, and applied mathematics, focusing on kinetic theory applications to gaseous systems, biological phenomena, and environmental engineering. Key research areas include chemotaxis modeling for Multiple Sclerosis, shock wave dynamics in gas mixtures, and optimal control strategies for waste treatment processes. His research explores both theoretical advancements and practical applications, such as deriving hydrodynamic limits from kinetic equations, analyzing reaction-diffusion systems with Allee effects, and developing numerical methods for complex fluid flows. Recent work addresses misinformation dynamics through prebunking strategies and investigates non-equilibrium phenomena in multi-component gas mixtures. Publications span multiscale modeling techniques, including hybrid kinetic models and IMEX finite volume schemes, alongside studies on thermal non-equilibrium in shock waves and optimal control of biocell composting systems. His interdisciplinary approach integrates mathematical rigor with real-world problem-solving in biomedical, environmental, and engineering domains.

Sylvie MARCOSمشاهده پروفایل

پژوهشگر

• Signal Processing
• Radar Systems
• Machine Learning
• +۶ مورد دیگر

Sylvie MARCOS is a Senior Researcher at CentraleSupélec, France, affiliated with the Laboratoire des Signaux et Systèmes (L2S). She holds a PhD in Telecommunications (1987) and an HDR in Signal Processing (1995) from the University of Paris-Sud. Her research focuses on signal processing, radar systems, machine learning applications in environmental monitoring, and chaotic sequence generation for telecommunications. She has supervised numerous doctoral students and co-authored over 50 publications in high-impact journals and conferences. Education: Engineer, Ecole Centrale de Paris (1984) PhD in Telecommunications, University of Paris-Sud (1987) Habilitation à Diriger des Recherches (HDR), University of Paris-Sud (1995) Research Interests: Time series analysis and anomaly detection Radar signal processing and LPI radar detection Chaotic sequences for MIMO radar and CDMA systems High-resolution source localization and sparsity-based methods Adaptive filtering and neural network applications Her recent work emphasizes environmental monitoring using machine learning and deep learning techniques, as well as robust radar systems design. She collaborates with institutions like CEA-DAM Valduc and Onera on defense-related signal processing challenges. Grants/Projects: Active involvement in European and national research projects on radar systems, environmental signal processing, and sparse signal recovery. Labs/Teams: Member of L2S research groups MODESTY, COMEDY, and SYCOMORE, focusing on systems modeling and robust control.

Daniel Jodlbauerمشاهده پروفایل

پژوهشگر

• Computational Mathematics
• Numerical Analysis
• Biomedical Imaging
• +۴ مورد دیگر

Dr. Daniel Jodlbauer is a Research Scientist at the Johann Radon Institute for Computational and Applied Mathematics (RICAM) , part of the Austrian Academy of Sciences (OEAW). He is affiliated with the Transfer Group , focusing on interdisciplinary computational methods and their applications in science and engineering. His research spans numerical analysis, computational mechanics, and biomedical imaging. Key projects include developing matrix-free multigrid solvers for fluid-structure interaction and phase-field fracture problems, and advancing adaptive optics for biomedical imaging (e.g., AO-OCT) and astronomical instrumentation (e.g., the MICADO imager for the Extremely Large Telescope). His work bridges theoretical computational methods with real-world applications in medicine, astronomy, and engineering. Recent contributions highlight advancements in PSF reconstruction for the MICADO project, improving imaging resolution for the ELT telescope, and optimizing wavefront sensors for retinal imaging. His computational methods address challenges in high-performance computing, parallel algorithms, and nonlinear solvers for complex physical systems. Dr. Jodlbauer collaborates across disciplines, contributing to RICAM’s research groups in Computational Methods for PDEs and Optimization. His publications reflect expertise in mathematical data science, inverse problems, and multivariate algorithms, though no academic awards or grants are explicitly noted in the provided text.

Bernhard Schuberthمشاهده پروفایل

پژوهشگر

• Computational Seismology
• Geodynamics
• Mantle Dynamics
• +۳ مورد دیگر

Bernhard Schuberth is a Senior Research Scientist in the Department of Earth and Environmental Sciences at Ludwig-Maximilians-Universität München, specializing in Geodynamics. His work bridges computational seismology, geodynamics, and mineral physics to understand Earth's deep structure and dynamics. He holds a PhD (summa cum laude, 2009) and a Habilitation (2023) in Geophysics from LMU Munich. His research focuses on mantle circulation models, seismic wave propagation, and tomographic filtering. Education: Diplom in Geophysics (2004, LMU Munich), PhD (2009), and Habilitation (2023). Awards include the Edison Award Silver Prize (2004) and a Marie-Curie Fellowship (2010–2012). Research Interests: Combining numerical simulations of seismic wave propagation with geodynamic models to test hypotheses about mantle dynamics. Key tools include spectral element methods, mantle flow modeling, and mineral physics-based phase transitions. Publications highlight: Recent work addresses uncertainties in mantle temperature estimates (2025), tomographic resolution (2024), and mantle flow trajectories (2023). Earlier studies validated thermal explanations for superplumes (2012) and post-perovskite constraints (2018). Awards and Grants: Recipient of grants from the Marie-Curie program and the German Excellence Network. Active in collaborative projects like PRACE DECI and GLOBALSEIS. Labs/Teams: Leads computational geophysics projects using high-performance computing. Collaborates with international groups on mantle dynamics and seismic modeling.

Avick Sinhaمشاهده پروفایل

پژوهشگر ارشد

• Multiphase Flows
• Fluid Dynamics
• Wound Dressings
• +۴ مورد دیگر

Dr. Avick Sinha is a Senior Research Fellow at the University of Brighton, affiliated with the Advanced Engineering Centre and the Centre for Regenerative Medicine and Devices. He holds a PhD from the Indian Institute of Technology Bombay (2013–2019) and has expertise in multiphase flows, free shear flows, and computational fluid dynamics (CFD) using OpenFOAM. His research focuses on developing advanced wound dressings and investigates flow dynamics in aerospace and biomedical contexts. Education: PhD in Mechanical Engineering, Indian Institute of Technology Bombay (2013–2019) Master’s in Mechanical Engineering, Indian Institute of Technology Kanpur (2009–2011) Bachelor’s in Mechanical Engineering, Kalinga Institute of Industrial Technology (2005–2009) Research Interests: Dr. Sinha’s work spans multiphase flows, fluid dynamics, and biomedical engineering. He employs experimental techniques like high-speed imaging and PIV/PLIF, alongside CFD simulations, to study droplet dynamics, heat transfer, and wound dressing efficacy. Key areas include: Wound exudate management and material characterization Optimization of biomedical materials for tissue repair Flow instability analysis in gas turbines and aero-engine bearings Awards: Prime Minister’s Fellowship for PhD (£35,000) Travel Grant for APS-DFD Meeting (£2,000) Doctoral Scholarship (MHRD, India) Research & Teaching Assistant Fellowship (£2,500) Grants & Projects: Principal Investigator on a £3.2M EPSRC grant for biomimetic wound dressing development (2022–2028). Previously involved in Rolls-Royce-funded projects (COAST, Cornerstone, AERIES) at the University of Nottingham. Labs & Teams: Affiliated with the Advanced Engineering Centre and the Centre for Regenerative Medicine and Devices, collaborating on biomaterials and fluid dynamics research.

• Computer Graphics
• Motion Capture
• Artificial Intelligence
• +۹ مورد دیگر

Dr. Zhidong Xiao serves as Principal Academic (Associate Professor) at Bournemouth University's National Centre for Computer Animation within the Faculty of Media and Communication. With over ten years of leadership experience including roles as Programme Leader, Head of Education, and Deputy Head of Department, he drives academic strategy and research innovation in computer animation and digital media. His work bridges technical excellence with creative industry applications through extensive collaborations across the UK and China. Dr. Xiao's educational foundation includes a PhD in Computer Graphics (2010) and postgraduate certificates in Education Practice (2010) and Research Degree Supervision (2011) from Bournemouth University, complemented by a BEng (Hons) in Thermodynamics from Taiyuan University of Technology, China (1994). PhD in Computer Graphics, Bournemouth University (2010) PGCE in Education Practice, Bournemouth University (2010) PGCE in Research Degree Supervision, Bournemouth University (2011) BEng (Hons) in Thermodynamics, Taiyuan University of Technology (1994) His research spans Computer Graphics, Motion Capture, Artificial Intelligence, and Virtual Reality with focus on physics-based simulation, sign language recognition, and motion synthesis. Recent work integrates partial differential equations with machine learning to solve animation challenges in facial realism, deformation simulation, and 3D reconstruction. His interdisciplinary approach connects computer science with creative industries, healthcare applications, and educational technology while advancing core techniques in neural rendering and motion analysis. Analysis of his 15 most recent publications reveals consistent innovation in physics-based animation techniques (40%), motion capture processing (25%), and neural approaches to 3D reconstruction (35%). Key trends include the fusion of analytical physics models with deep learning architectures, development of efficient real-time simulation methods, and expansion into accessibility applications through sign language recognition systems. Scientific recognitions include: Fellow of British Computer Society (2023) Fellow of Higher Education Academy (2011) Best Poster Award at Pacific Graphics 2014 He maintains active peer review roles for EPSRC, ESRC, IEEE Transactions on Multimedia, and ACM SIGGRAPH conferences. Dr. Xiao has supervised seven PhD students to completion while currently guiding Alexandra Sergeeva Alexdottir's research on Phantom Touch phenomena. His grant portfolio demonstrates strong industry-academia collaboration: Principal Investigator Capturing and representing sign language (British Council, 2025) VE Communication Programme (Erasmus+, 2020) Co-Investigator Rehabilitation Enhancement via Motion Capture (BU Fusion Fund, 2013) Cross-Channel Film Lab (Interreg, 2012) Digital Beijing Opera Project (2010) As a core member of Bournemouth's Computer Graphics and Visualisation Research Group and Centre for Digital Entertainment, he leads initiatives in motion capture technology through AccessMocap Studio. His international outreach includes invited lectures across China on computer animation education and visual effects techniques, strengthening global partnerships in creative technology development.