Chanyuan Jin | Regeneration | Research Excellence Award

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Prof. Chanyuan Jin | Regeneration | Research Excellence Award

Peking University School and Hospital for Stomatology | China

Dr. Chanyuan Jin is an Associate Research Fellow at the Peking University School and Hospital for Stomatology, with advanced expertise in prosthodontics, stem cell biology, and regenerative medicine. She is widely recognized as a young scientific talent at both national and regional levels and leads cutting-edge research on stem cell osteogenic differentiation and bone tissue regeneration. Her work focuses on developing innovative biomaterials and tissue engineering strategies for hard and soft tissue repair, with strong translational relevance to dental and orthopedic applications. Dr. Jin has secured multiple highly competitive research grants and has authored more than 20 peer-reviewed scientific publications, along with contributions to academic book chapters. Her research excellence has been acknowledged through prestigious awards, including international recognition in tissue engineering. In addition to her academic achievements, she actively bridges industry and academia through collaborative training and research programs with leading biomedical companies, guiding postdoctoral researchers in translational and product-oriented studies. Dr. Jin also holds several patents related to medical imaging analysis, biomaterials, and regenerative technologies. She serves on editorial boards of respected international and national journals and contributes to scientific peer review and academic leadership. Through her interdisciplinary approach, clinical insight, and mentorship, Dr. Jin continues to make significant contributions to advancing regenerative dentistry and craniofacial rehabilitation.

Citation Metrics (Scopus)

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Citations
1048

Documents
35
h-index
15

Citations

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h-index


View Scopus Profile

Featured Publications


Dual-responsive nanoparticle system for enhanced blood-brain barrier crossing and glioblastoma penetration

– Biomaterials

Optimizing hydrogen peroxide shock treatment frequencies for dental unit waterlines contamination control: a pilot study

– BMC Oral Health

Fast response cathodic electrochemiluminescence sensor based on closed bipolar electrode for point-of-care blood glucose testing

– Talanta

High-Entropy Environments Enable Metal Surface-Catalyzed Nucleophilic Electrooxidation

– Angew Chem Int Ed Engl

Integrated trilayer nanofiber membrane with tailored antibacterial, antioxidant, and mechanical properties for accelerated infected wound healing

– Nano Research

Zhifeng Chen | Biomedical Engineering | Excellence in Innovation Award

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Dr. Zhifeng Chen | Biomedical Engineering | Excellence in Innovation Award 

Neusoft Medical Systems Co. Ltd | Australia

Zhifeng Chen, Ph.D., is an accomplished biomedical imaging scientist whose multidisciplinary career spans advanced MRI physics, deep learning–driven neuroimaging, quantitative MRI, and accelerated data acquisition methodologies. With a strong academic foundation from Zhejiang University and extensive postdoctoral training at premier institutions including Harvard Medical School, Massachusetts General Hospital, the University of Southern California, and Monash University he has developed a robust research portfolio focused on rapid, motion-resistant, and high-fidelity MRI techniques. Dr. Chen has made notable contributions to non-Cartesian reconstruction, ASL angiography, fMRI, susceptibility mapping, and liver DCE-MRI, integrating compressed sensing, low-rank subspace modeling, diffusion models, contrastive learning, and untrained neural networks. His expertise extends to pulse sequence programming on Siemens 3T and 7T platforms, advanced signal processing, and deep learning–based image reconstruction. A productive researcher with 26 documents, 214 citations, and an h-index of 9, he has demonstrated sustained scholarly impact across biomedical engineering, radiology, and computational imaging. His work has been recognized through competitive fellowships and prestigious awards, including multiple ISMRM Merit Awards, the International Postdoctoral Exchange Fellowship, and the Shandong Provincial Science and Technology Progress Prize. Dr. Chen has secured numerous grants as Principal Investigator, leading innovations in physics-informed deep learning, motion-corrected dynamic imaging, and quantitative MRI. His professional service includes reviewing for leading journals, coordinating special issues, and evaluating national and international research grants. With extensive experience mentoring students and collaborating across academia and industry, Dr. Chen continues to advance next-generation MRI technologies that enhance imaging speed, robustness, and clinical diagnostic value.

Profile: Scopus | Orcid

Featured Publications 

Chen, Z. (2025). Accelerating multi-directional diffusion MRI through patch-based joint reconstruction. NeuroImage.

Ekanayake, M., Pawar, K., Chen, Z., Egan, G., & Chen, Z. (2025). PixCUE: Joint uncertainty estimation and image reconstruction in MRI using deep pixel classification. Journal of Imaging Informatics in Medicine.

Pouria Mazinani | Biomechanics | Young Scientist Award

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Dr. Pouria Mazinani | Biomechanics | Young Scientist Award 

University of Catania | Iran

Dr. Pouria Mazinani is a highly skilled Project Manager and researcher with a multidisciplinary background in Mechanical and Civil Engineering. He holds multiple advanced degrees, including a Ph.D. in Civil Engineering from the University of Catania and dual MSc degrees in Civil and Mechanical Engineering. His research and technical expertise span biomechanics, structural analysis, computational modeling, and fluid mechanics, with a particular focus on the simulation of corneal Young’s modulus using ABAQUS, Python, and Elastography. Dr. Mazinani has demonstrated strong analytical and leadership abilities through his roles at IDRA Quality Inspection Company and IQI Group, where he managed engineering projects, conducted mechanical equipment inspections, and ensured compliance with international quality standards. His academic contributions include five research documents, accumulating 13 citations and an h-index of 2, reflecting his growing impact in the engineering research community. Proficient in a wide range of software tools such as ANSYS, COMSOL, MATLAB, and AutoCAD, he also holds numerous professional certifications in quality management systems, data science, gas turbines, and project management. Dr. Mazinani’s combination of technical expertise, project management acumen, and research excellence underscores his commitment to advancing innovative engineering solutions.

Profile: Scopus 

Featured Publications 

Mazinani, P. (2025). Evaluating corneal biomechanics using intraocular pressure methods and finite element modeling: Parameters study and parametric optimization. Zeitschrift für Angewandte Mathematik und Physik.

Shaokun Ge | Engineering | Best Researcher Award 

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Dr. Shaokun Ge | Engineering | Best Researcher Award 

China Academy of Safety Science and Technology | China

Shaokun Ge is a Chinese researcher whose work focuses on the thermal and toxic hazards associated with vehicle‐fires on bridges and the consequent implications for structure, rescue operations and traffic strategy. Having earned a BSc in Mining Engineering followed by a D.C. in Safety Engineering, he has developed a profile around four main research strands: the burning behaviour of bridge vehicle fires (including influences of fire type/scale, ambient wind, and decker spacing/sound-barriers); thermal damage to bridge structures and to personnel (through temperature prediction models and fire-resistant materials for key load-bearing elements, as well as thermal‐hazard evaluation from the standpoint of escape and rescue); toxic-gas generation and distribution in bridge‐fire scenarios (addressing asphyxiating/irritating gases, horizontal/vertical distribution at human height, and toxicity-evaluation modelling); and traffic management plus rapid extinguishing systems for bridge fires (e.g., controlling extremely hazardous vehicles, fire-fighting systems employing river water, and micro-foam extinguishing approaches). As of now, his h-index stands at — and his publication and citation counts are —, reflecting that bibliometric details are still being assembled/verified. His work integrates bridge engineering, fire safety, toxicology, and traffic management, aiming to enhance resilience and rapid response in the event of bridge vehicle‐fires.

Profile: Scopus 

Featured Publications 

Du, G., Liu, G., Ni, Y., Xu, B., & Ge, S. (2025). Fire-induced temperature response of main cables and suspenders in suspension bridges: 1:4-scaled experimental and numerical study. Case Studies in Thermal Engineering, 68, 105878.

Xiaohan Xing | Biomedical Engineering | Young Scientist Award

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Dr. Xiaohan Xing | Engineering | Young Scientist Award

Dr. Xiaohan Xing, Stanford University, United States.

Dr. Xiaohan XING is a Postdoctoral Researcher at Stanford University specializing in Biomedical AI, medical image analysis, and deep learning. 🎓 He holds a Ph.D. in Electronic Engineering from CUHK and has received prestigious awards, including the MICCAI Young Scientist Award (2022) 🏆 and ASTRO Best of Physics Award (2024) 🥇. His research focuses on developing efficient AI models for disease diagnosis 🏥📊. As an academic leader, he has served as MICCAI 2024 Area Chair and a reviewer for top-tier journals. ✨

🌟 Professional Profile

🎓 Early Academic Pursuits

Dr. Xiaohan Xing began his academic journey at Shandong University, where he pursued a B.E. in Biomedical Engineering with outstanding academic performance, ranking 1st among 200+ students. His undergraduate thesis, Bleeding Detection in Wireless Capsule Endoscopy Images, earned him the Outstanding Undergraduate Thesis Award. Following this, he pursued a Ph.D. in Electronic Engineering at The Chinese University of Hong Kong (CUHK) under the supervision of Prof. Max Q.-H. Meng and Prof. Hongsheng Li. His doctoral research focused on efficient deep learning algorithms for disease diagnosis, a field at the intersection of medical imaging, artificial intelligence, and healthcare.

💼 Professional Endeavors

Dr. Xing has held prestigious postdoctoral positions at leading institutions. He is currently a Postdoctoral Fellow at Stanford University’s Department of Radiation Oncology, working under Prof. Lei Xing. Prior to this, he served as a Postdoctoral Fellow at City University of Hong Kong (CityU), collaborating with Prof. Yixuan Yuan. He also gained industry experience as a Research Intern at Tencent AI Healthcare, where he worked under Dr. Jianhua Yao and Dr. Fan Yang to develop AI-driven healthcare solutions.

🔬 Contributions and Research Focus On Biomedical Engineering

Dr. Xing’s research is centered on Biomedical AI, integrating medical imaging, deep learning, omics analysis, and data science to enhance clinical decision-making. His key research contributions include:

  • Data-efficient algorithms for medical image analysis 🏥

  • Interpretable AI models for omics data analysis 🧬

  • Multi-modal learning approaches integrating medical images and genomic data 🖼️➕📊

  • Trustworthy AI models for precision medicine applications 🏥

His innovative solutions have tackled challenges such as modality gaps in multi-modal learning, low-field MRI reconstruction, small lesion detection in endoscopy images, and cancer survival prediction. His research outputs have been published in top-tier journals such as Medical Image Analysis (MedIA), IEEE Transactions on Medical Imaging (TMI), and Bioinformatics.

🌍 Impact and Influence

Dr. Xing’s work significantly impacts precision medicine and AI-assisted healthcare. His publications in top-tier journals and conferences, including Medical Image Analysis (MedIA) and MICCAI, showcase his groundbreaking innovations. He has also contributed to patents in AI-driven disease detection.

🏅 Awards and Honors 

Dr. Xing’s excellence in research has been recognized with prestigious awards, including:

  • ASTRO Best of Physics 2024 (Ranked 1st among medical imaging papers, top 5 out of 1,650).

  • MICCAI Young Scientist Award 2022 (Equivalent to Best Paper Award, only 5 recipients worldwide).

  • National Scholarship (China Ministry of Education, Top 1%), the highest honor for students in China.

  • Outstanding Undergraduate Thesis Award (SDU, Top 1%).

  • First-class and Full Postgraduate Scholarships, recognizing academic excellence.
    These honors highlight his exceptional contributions to medical AI and deep learning, positioning him as a rising leader in the field.

🎒 Teaching and Mentorship

Dr. Xing has actively contributed to academia as a teaching assistant for:

  • Digital Circuits and Systems
  • Linear Algebra for Engineers
  • Digital Image Processing

Additionally, he has mentored Ph.D. and master’s students from CUHK, UCLA, and the University of Chinese Academy of Sciences.

📚 Academic Citations

Dr. Xing has been recognized in leading scientific communities, serving as an Area Chair for MICCAI 2024 and a reviewer for IEEE TMI, IEEE TIP, and MedIA. His papers are highly cited, reflecting his influence in AI-powered medical research.

🚀Future Vision and Impact

Dr. Xing envisions pioneering AI-driven healthcare by developing trustworthy and interpretable AI models. His future work aims to bridge the gap between medical imaging and omics data for next-generation precision medicine. As a rising leader in Biomedical AI, his legacy is set to transform medical diagnostics and patient care.

Publications Top Notes

 

  • 🏥 Automatic Polyp Recognition in Colonoscopy Images Using Deep Learning and Two-Stage Pyramidal Feature Prediction
    📖 IEEE Transactions on Automation Science and Engineering
    👥 Cited by: 108 📆 Year: 2020

  • 🔬 DT-MIL: Deformable Transformer for Multi-Instance Learning on Histopathological Images
    📖 Medical Image Computing and Computer Assisted Intervention (MICCAI)
    👥 Cited by: 97 📆 Year: 2021

  • 🎥 Wireless Capsule Endoscopy: A New Tool for Cancer Screening in the Colon with Deep-Learning-Based Polyp Recognition
    📖 Proceedings of the IEEE
    👥 Cited by: 90 📆 Year: 2019

  • 🩺 Zoom in Lesions for Better Diagnosis: Attention Guided Deformation Network for WCE Image Classification
    📖 IEEE Transactions on Medical Imaging
    👥 Cited by: 48 📆 Year: 2020

  • 🏥 Categorical Relation-Preserving Contrastive Knowledge Distillation for Medical Image Classification
    📖 Medical Image Computing and Computer Assisted Intervention (MICCAI)
    👥 Cited by: 45 📆 Year: 2021

  • 🩸 Bleeding Detection in Wireless Capsule Endoscopy Image Video Using Superpixel-Color Histogram and a Subspace KNN Classifier
    📖 IEEE Engineering in Medicine and Biology Society Conference
    👥 Cited by: 44 📆 Year: 2018

  • 🧬 Multi-Level Attention Graph Neural Network Based on Co-Expression Gene Modules for Disease Diagnosis and Prognosis
    📖 Bioinformatics
    👥 Cited by: 40 📆 Year: 2022

  • 🏥 Multi-Modal Multi-Instance Learning Using Weakly Correlated Histopathological Images and Tabular Clinical Information
    📖 Medical Image Computing and Computer Assisted Intervention (MICCAI)
    👥 Cited by: 33 📆 Year: 2021

  • 🧠 Discrepancy and Gradient-Guided Multi-Modal Knowledge Distillation for Pathological Glioma Grading
    📖 Medical Image Computing and Computer-Assisted Intervention (MICCAI)
    👥 Cited by: 28 📆 Year: 2022

  • 🩸 A Saliency-Aware Hybrid Dense Network for Bleeding Detection in Wireless Capsule Endoscopy Images
    📖 IEEE International Symposium on Biomedical Imaging (ISBI)
    👥 Cited by: 21 📆 Year: 2019

  • 🤖 Medical Federated Learning with Joint Graph Purification for Noisy Label Learning
    📖 Medical Image Analysis
    👥 Cited by: 18 📆 Year: 2023

  • 🩸 Discrepancy-Based Active Learning for Weakly Supervised Bleeding Segmentation in Wireless Capsule Endoscopy Images
    📖 Medical Image Computing and Computer-Assisted Intervention (MICCAI)
    👥 Cited by: 18 📆 Year: 2022

  • 🧬 An Interpretable Multi-Level Enhanced Graph Attention Network for Disease Diagnosis with Gene Expression Data
    📖 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
    👥 Cited by: 17 📆 Year: 2021

  • 👁 Boundary-Enhanced Semi-Supervised Retinal Layer Segmentation in Optical Coherence Tomography Images Using Fewer Labels
    📖 Computerized Medical Imaging and Graphics
    👥 Cited by: — 📆 Year: 2023