Yanjie Zhu | Algorithm Development | Best Researcher Award

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Prof. Yanjie Zhu | Algorithm Development | Best Researcher Award

Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China

👨‍🎓Professional Profiles

🔬 Summary

Prof. Yanjie Zhu is a renowned expert in fast magnetic resonance imaging (MRI) and its applications in medical diagnostics. He specializes in developing innovative imaging techniques through machine learning, deep learning, and model-driven methods. As a Professor at the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, he leads cutting-edge research projects focused on cardiovascular imaging, brain health, and intelligent diagnostic tools.

🎓 Education

Prof. Zhu earned his Ph.D. in Circuits and Systems from the Shanghai Institute of Technical Physics, Chinese Academy of Sciences, China (2006-2011). He also holds a B.S. in Electronic Engineering and Information Science from the University of Science and Technology of China, Hefei (2002-2006).

🏫 Professional Experience

Prof. Zhu has a distinguished academic career at the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, where he progressed from Assistant Professor (2011-2015) to Associate Professor (2015-2020), and is currently a Professor (2020-present). Additionally, he served as a Visiting Scholar (2017-2018) at Harvard Medical School and Beth Israel Deaconess Medical Center, Boston, MA, under the guidance of Reza Nezafat.

📚 Academic Contributions

Prof. Zhu has published several impactful papers in the field of MRI and medical imaging. Notable works include:

  • “Online reconstruction of fast dynamic MR imaging using deep low-rank plus sparse network,” IEEE CBMS, 2022.
  • “k-space based reconstruction method for wave encoded bSSFP sequence,” CECIT, 2021.
  • “Quantification of pectinate muscles inside left atrial appendage from CT images using fractal analysis,” ICMIPE, 2021.
  • Myocardial Edema Imaging – A Comparison of Three Techniques, ISMRM 2018 (Power Pitch, Magna Cum Laude Merit Award).

🔍 Research Interests

Prof. Zhu’s research interests revolve around model-driven fast MRI techniques, generative model-based adaptive MRI methods, and diffusion tensor and edema imaging for myocardial and brain tissue. He is also focused on intelligent diagnosis for the early detection of stroke-related plaques and other cardiovascular conditions.

💻 Technical Skills

Prof. Zhu is highly skilled in MRI imaging techniques, including fast MRI, cardiac MRI, and brain MRI. He also excels in deep learning and AI-based image reconstruction methods, along with proficiency in medical imaging software and model-driven approaches. His expertise in data analysis and computational methods has contributed to advancing medical applications.

👨‍🏫 Teaching Experience

Throughout his career, Prof. Zhu has mentored and taught graduate students and professionals in the fields of MRI technology, medical imaging, and computational healthcare methods. His research-driven approach has helped develop comprehensive educational materials that support the training of professionals in advanced imaging systems.

📖Top Noted Publications

RS-MOCO: A deep learning-based topology-preserving image registration method for cardiac T1 mapping

Authors: Chiyi Huang, Longwei Sun, Dong Liang, Haifeng Liang, Hongwu Zeng, Yanjie Zhu
Journal: Computers in Biology and Medicine
Year: 2024

High-Frequency Space Diffusion Model for Accelerated MRI

Authors: Chentao Cao, Zhuo-Xu Cui, Yue Wang, Shaonan Liu, Taijin Chen, Hairong Zheng, Dong Liang, Yanjie Zhu
Journal: IEEE Transactions on Medical Imaging
Year: 2024

SPIRiT-Diffusion: Self-Consistency Driven Diffusion Model for Accelerated MRI

Authors: Zhuo-Xu Cui, Chentao Cao, Yue Wang, Sen Jia, Jing Cheng, Xin Liu, Hairong Zheng, Dong Liang, Yanjie Zhu
Journal: IEEE Transactions on Medical Imaging
Year: 2024

A Two-Stage Generative Model with CycleGAN and Joint Diffusion for MRI-based Brain Tumor Detection

Authors: Wenxin Wang, Zhuo-Xu Cui, Guanxun Cheng, Chentao Cao, Xi Xu, Ziwei Liu, Haifeng Wang, Yulong Qi, Dong Liang, Yanjie Zhu
Journal: IEEE Journal of Biomedical and Health Informatics
Year: 2024

High efficiency free-breathing 3D thoracic aorta vessel wall imaging using self-gating image reconstruction

Authors: Caiyun Shi, Congcong Liu, Shi Su, Haifeng Wang, Xin Liu, Hairong Zheng, Dong Liang, Yanjie Zhu
Journal: Magnetic Resonance Imaging
Year: 2024

Physics-Driven Deep Learning Methods for Fast Quantitative Magnetic Resonance Imaging

Authors: Yanjie Zhu, Jing Cheng, Zhuo-Xu Cui, et.al.
Journal: IEEE Signal Processing Magazine
Year: 2023

Jing An | Artificial Intelligence | Best Researcher Award

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Dr. Jing An | Artificial Intelligence | Best Researcher Award

Yancheng Institute of Technology, China

👨‍🎓Professional Profile

Scopus Profile

👨‍🏫 Summary

Dr. Jing An is a distinguished professor and master tutor at Yancheng Institute of Technology, China. He specializes in intelligent manufacturing engineering, industrial big data fault diagnosis, and residual life prediction. With numerous patents and software copyrights, Dr. An has published over 20 SCI/EI indexed papers and contributed to key academic texts. His pioneering research in artificial intelligence-based fault diagnosis has earned him prestigious awards, including first-place recognition in the China Commerce Federation Science and Technology Award .

🎓 Education

Dr. An holds a Ph.D. in Computer Science from Hohai University (2021) and a Master’s degree in Computer Science from Harbin University of Science and Technology (2006). His academic foundation has strongly influenced his research in AI and fault diagnosis.

💼 Professional Experience

Having joined Yancheng Institute of Technology in 2006, Dr. An is also the Vice President of Science and Technology for Jiangsu Province’s “Double Innovation Plan.” He has led numerous provincial-level projects, and his expertise has extended to more than 10 industry partnerships 🚀.

📚 Academic Citations

Dr. An has authored over 20 peer-reviewed papers in prestigious journals such as IEEE Access and Mathematical Problems in Engineering. His impactful research on AI-based fault diagnosis methods is frequently cited within the academic community .

🔧 Technical Skills

Dr. An is skilled in AI-based fault diagnosis, deep learning, machine learning, and industrial big data analytics. He has expertise in Convolutional Neural Networks (CNNs) and intelligent manufacturing systems, focusing on improving machinery reliability and efficiency .

🧑‍🏫 Teaching Experience

Dr. An has been recognized as an outstanding teacher twice at Yancheng Institute of Technology. He teaches courses in intelligent manufacturing engineering and industrial big data fault diagnosis, preparing students to advance in the field of AI and data science .

🔍 Research Interests

Dr. An’s research is focused on developing intelligent systems for fault diagnosis in rotating machinery, predictive maintenance, and the application of deep learning techniques in industrial big data. His work aims to enhance manufacturing processes and equipment reliability .

📖Top Noted Publications

Hybrid Mechanism and Data-Driven Approach for Predicting Fatigue Life of MEMS Devices by Physics-Informed Neural Networks

Authors: Cheng, J., Lu, J., Liu, B., An, J., Shen, A.

Journal: Fatigue and Fracture of Engineering Materials and Structures

Year: 2024

Bearing Intelligent Fault Diagnosis Based on Convolutional Neural Networks

Authors: An, J., An, P.

Journal: International Journal of Circuits, Systems and Signal Processing

Year: 2022

Deep Clustering Bearing Fault Diagnosis Method Based on Local Manifold Learning of an Autoencoded Embedding

Authors: An, J., Ai, P., Liu, C., Xu, S., Liu, D.

Journal: IEEE Access

Year: 2021

Deep Domain Adaptation Model for Bearing Fault Diagnosis with Riemann Metric Correlation Alignment

Authors: An, J., Ai, P.

Journal: Mathematical Problems in Engineering

Year: 2020

Deep Domain Adaptation Model for Bearing Fault Diagnosis with Domain Alignment and Discriminative Feature Learning

Authors: An, J., Ai, P., Liu, D.

Journal: Shock and Vibration

Year: 2020

Giuliana Ramella | Artificial Intelligence | Best Researcher Award

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Dr. Giuliana. Ramella | Artificial Intelligence | Best Researcher Award

National Research Council, Italy

Professional Profile 👨‍🎓

Scopus Profile

Orcid Profile

Research Gate Profile

Early Academic Pursuits 🎓

Dr. Giuliana Ramella embarked on her academic journey with a strong foundation in Physics, specializing in Cybernetics, from the University of Naples “Federico II”, Italy, where she obtained her Laurea degree in 1990. Her early career was distinguished by a fellowship granted by the Italian National Research Council (CNR) at the Institute of Cybernetics “E. Caianiello”, which marked the beginning of her deep engagement with interdisciplinary research. Her academic background was further enriched by participation in several international and national schools, covering topics from biophysics to machine vision, which laid the groundwork for her future research endeavors.

Professional Endeavors 🧑‍🔬

Dr. Ramella’s professional career at the CNR has spanned decades, beginning in 1991 with a fellowship at the Institute of Cybernetics, now known as the Institute of Applied Sciences and Intelligent Systems (CNR-ISASI). Over the years, she transitioned into permanent research roles, contributing significantly to numerous research projects. Her professional milestones include being a visiting researcher at LIAMA (Sino-French Laboratory) in Beijing, China, and overseeing major research initiatives such as those focused on image processing and the conservation of cultural heritage.

Contributions and Research Focus 🔬

Dr. Ramella’s research spans multiple fields, including image processing, artificial intelligence, neurosciences, and cultural heritage conservation. Notable contributions include leadership in projects such as CNR-IAC-CNR DIT.AD021.077 (focused on color image processing) and the Campania Imaging Infrastructure for Research in Oncology. Her work blends theoretical and practical applications, particularly in the intersection of machine learning and image analysis, demonstrating her commitment to advancing computational methods in complex scientific domains.

Impact and Influence 🌍

Dr. Ramella has made a significant impact both in Italy and internationally, particularly in the fields of biophysics, neurosciences, and cultural heritage preservation. She has led several high-profile projects, influencing the development of automated systems for monitoring and diagnosing cultural heritage, as well as data analysis systems for oncology research. Her leadership in educational coordination has also contributed to the professional development of individuals in specialized fields, including image and data management.

Academic Citations and Scholarly Recognition 📚

Throughout her career, Dr. Ramella has built a robust academic reputation, frequently cited for her work in machine learning frameworks like Pytorch, TensorFlow, and Keras, as well as her contributions to computer vision. Her involvement in international workshops and conferences further underscores her standing as a thought leader in the scientific community. Additionally, her research has had a profound effect on the fields of visual perception and neuroscience, solidifying her as a key figure in these interdisciplinary areas.

Technical Skills and Expertise 💻

Dr. Ramella is highly skilled in a range of programming languages, including Matlab, C/C++, and Python, which are essential tools for her work in data analysis and machine learning. She has a strong command over popular machine learning frameworks like Pytorch, TensorFlow, and Keras, which she applies to advanced research in image processing, signal analysis, and high-dimensional data modeling. Her technical expertise is fundamental to her contributions to automated systems in the analysis of cultural artifacts and medical imaging.

Teaching Experience 🍎

In addition to her research, Dr. Ramella has played an essential role in educational coordination. She co-led specialist courses for unemployed individuals and workers in mobility, aimed at developing technical skills for sectors like image management and building heritage monitoring. Her role in shaping the professional development of students in the fields of image analysis and information technology has left a lasting impact on both the academic and professional communities.

Legacy and Future Contributions 🔮

Looking ahead, Dr. Ramella’s legacy is poised to continue making waves in the fields of artificial intelligence and machine learning, especially in the areas of healthcare, cultural heritage conservation, and data-driven methodologies. Her ongoing involvement in projects like the Agritech research program, funded by the European Union through Next Generation EU, speaks to her future aspirations to contribute to cutting-edge research and technological advancements. Dr. Ramella’s future work promises to leave an indelible mark on interdisciplinary fields, continuing her legacy of innovation and impact across global scientific communities.

 

Top Noted Publications 📖

An Open Image Resizing Framework for Remote Sensing Applications and Beyond

Authors: Occorsio, D., Ramella, G., Themistoclakis, W.
Journal: Remote Sensing
Year: 2023

Image Scaling by de la Vallée-Poussin Filtered Interpolation

Authors: Occorsio, D., Ramella, G., Themistoclakis, W.
Journal: Journal of Mathematical Imaging and Vision
Year: 2023

Filtered Polynomial Interpolation for Scaling 3D Images

Authors: Occorsio, D., Ramella, G., Themistoclakis, W.
Journal: Electronic Transactions on Numerical Analysis
Year: 2023

 Lagrange–Chebyshev Interpolation for Image Resizing

Authors: Occorsio, D., Ramella, G., Themistoclakis, W.
Journal: Mathematics and Computers in Simulation
Year: 2022

Saliency-based Segmentation of Dermoscopic Images Using Colour Information

Author: Ramella, G.
Journal: Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization
Year: 2022

Shubing Dai | Algorithm Development | Best Researcher Award

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Prof. Shubing Dai | Algorithm Development | Best Researcher Award

Prof. Shubing Dai at Northwest A&F University, China

Professional Profile👨‍🎓

👨‍🏫 Summary

Prof. Shubing Dai is an Associate Professor at Northwest A&F University, specializing in Hydraulic Engineering. With a background in Hydraulics and River Dynamics, he has published over 20 research papers and actively participated in over 20 research projects. His work primarily focuses on urban flood management, hydraulic structures, and water flow dynamics. He is also an active member of key professional organizations, including the China Society for Hydroelectric Engineering and the International Association for Hydro-Environment Engineering and Research (IAHR).

🎓 Education

Prof. Dai began his academic career at Northwest A&F University, where he earned his Bachelor’s (2008–2012) and Master’s (2012–2015) degrees in Hydraulic and Hydroelectric Engineering. He then pursued a Ph.D. in Hydraulics and River Dynamics at Dalian University of Technology (2016–2021). Additionally, he worked as a visiting scholar at the University of La Coruña in Spain (2024).

💼 Professional Experience

Prof. Dai has extensive professional experience, both in academia and industry. He currently serves as an Associate Professor at Northwest A&F University and the Department Secretary of the Hydraulic Engineering Department. He also previously worked as an Assistant Engineer at the Changjiang Survey, Planning, and Design Institute (2015–2016), contributing to several major hydropower and hydraulic engineering projects.

📚 Academic Contributions

Prof. Dai is an active contributor to the academic community, serving as a reviewer for several leading journals such as Journal of Hydrology and Physics of Fluids. He also serves as an editorial board member for an international journal. His research focuses on water resources, hydraulic structures, and flow dynamics, with over 20 published papers and several high-impact research projects.

🔧 Technical Skills

Prof. Dai has developed a range of technical skills, including:

  • Hydraulic Engineering: Focus on energy dissipation and optimization of flood discharge systems.
  • Urban Flooding & Drainage: Expertise in urban drainage system design, stormwater management, and flood mitigation.
  • Flow Dynamics: Proficient in Computational Fluid Dynamics (CFD) and non-steady flow simulations for hydraulic applications.
  • Hydropower Engineering: In-depth research on dam-break flood modeling, hydraulic structures, and their interactions with water flow.

👩‍🏫 Teaching Experience

As a dedicated educator, Prof. Dai mentors master’s students and teaches courses on fluid dynamics, hydraulic modeling, and water resource management. He is also responsible for the administration of the Water Resources and Hydroelectric Engineering department at Northwest A&F University, playing a key role in curriculum development and academic planning.

🔬 Research Interests

Prof. Dai’s research interests are focused on:

  • Hydraulic Engineering: Research on energy dissipation in hydraulic structures and optimization of flood discharge processes.
  • Flood Disaster Management: Urban flood management, drainage systems, and dam-break flood evolution.
  • Non-steady Flow & Hydrodynamics: Investigating the dynamics of free-surface flows, wave behavior, and non-constant flow conditions in hydraulic systems.

🔍 Research Projects

Prof. Dai leads and participates in several important research projects, such as:

  • City Flood and Drainage Systems Simulation (2022–2025)
  • Optimization of Urban Drainage Under Extreme Rainfall Conditions (2023–2025)
  • Dam-Break Flood Evolution and Structural Interaction Studies (2024–2026)
    He is also involved in national and regional projects on hydraulic structure optimization, flood forecasting, and water resource management.

 

📖Top Noted Publications

Numerical study of roll wave development for non-uniform initial conditions using steep slope shallow water equations

Authors: Dai, S., Liu, X., Zhang, K., Liu, H., Jin, S.
Journal: Physics of Fluids
Year: 2024

Discharge coefficients formulae of grate inlets of complicated conditions for urban floods simulation and urban drainage systems design

Authors: Dai, S., Hou, J., Jin, S., Hou, J., Liu, G.
Journal: Journal of Hydrology
Year: 2023

Land Degradation Caused by Construction Activity: Investigation, Cause and Control Measures

Authors: Dai, S., Ma, Y., Zhang, K.
Journal: International Journal of Environmental Research and Public Health
Year: 2022

Numerical investigations of unsteady critical flow conditions over an obstacle using three models

Authors: Dai, S., Jin, S.
Journal: Physics of Fluids
Year: 2022

Interception efficiency of grate inlets for sustainable urban drainage systems design under different road slopes and approaching discharges

Authors: Dai, S., Jin, S., Qian, C., Ma, Y., Liang, C.
Journal: Urban Water Journal
Year: 2021

Yun Xing | Computer Vision | Best Researcher Award

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Mr. Yun Xing | Computer Vision | Best Researcher Award

Mr. Yun Xing at The First Affiliated Hospital of Xi’an Jiaotong University, China

👨‍🎓Professional Profile

🎓 Education and Academic Background

I am currently a second-year Ph.D. student at VI-Lab, Nanyang Technological University (NTU), under the supervision of Prof. Shijian Lu. I received my B.S. degree in Biomedical Engineering and Instrument Science from Zhejiang University in 2021, where I worked under the guidance of Prof. Hong Zhou. Prior to my Ph.D., I completed my Master’s in Artificial Intelligence at NTU (2021–2022). My research interests primarily focus on vision-language pre-training and foundation model adaptation.

🔬 Research Interests

My research revolves around cutting-edge topics in vision-language pre-training and the adaptation of foundation models. I am particularly interested in methods that enable models to transfer knowledge effectively across different domains, such as few-shot learning, cross-domain adaptation, and improving model robustness in complex vision-language tasks.

🏆 Achievements and News

In recent years, I have been fortunate to have several papers accepted at top-tier conferences. These include NeurIPS 2024, ECCV 2024, CVPR 2024, and NeurIPS 2023, marking significant milestones in my academic journey. Notably, my work on object hallucination mitigation and segmentation adaptation has received considerable attention in the community.

🏅 Awards and Honors

Throughout my academic journey, I have received various recognitions for my contributions to research. These include the Outstanding Graduate Award from Zhejiang University in 2021, as well as the Academic Excellence Award (2018, 2019) and Academic Progress Award (2019) from the same institution.

💻 Service and Teaching

As an active member of the academic community, I contribute as a conference reviewer for prominent venues such as CVPR, ICML, ECCV, and NeurIPS 2024, where I was honored to be selected as one of the Top Reviewers at NeurIPS. Additionally, I am involved in teaching and mentoring at NTU. In Spring 2024, I will be assisting with the SC1015: Introduction to Data Science and Artificial Intelligence course.