Zisheng Guo | Algorithm Development | Best Researcher Award

Dr. Zisheng Guo | Algorithm Development | Best Researcher Award

Dr. Zisheng Guo | Algorithm Development – beijing university of technology, China

Dr. Zisheng Guo is a distinguished researcher and academic from Beijing University of Technology, specializing in harmonic magnetic fields, targeted magnetic moment enhancement, and magnetic field focusing techniques. His research primarily focuses on the application of advanced magnetic sensing methods for the detection of defects in steel pipes, particularly in challenging environments. With a strong background in innovative engineering solutions, Dr. Guo has developed novel systems for real-time defect detection, which have practical implications in industries like construction, oil and gas, and civil infrastructure. His contributions have led to improvements in detection accuracy, especially for pipes with large burial depths or thick cladding. With multiple publications and active involvement in cutting-edge research, Dr. Guo is making a significant impact in the field of magnetic sensing technologies.

Profile:

Scopus

Education:

Dr. Zisheng Guo holds a Ph.D. in Engineering, obtained from Beijing University of Technology, where he specialized in electromagnetic technology and sensor systems. His academic journey began with a Bachelor’s degree in Electrical Engineering, followed by a Master’s in Electromagnetic Field and Microwave Technology. Through his doctoral research, he developed a keen interest in magnetic field applications for material defect detection. His doctoral thesis focused on improving the accuracy and reliability of steel pipe defect detection using novel magnetic field techniques, laying the groundwork for his future research endeavors. Throughout his educational career, Dr. Guo’s work has been marked by innovation and a commitment to advancing practical engineering solutions, contributing significantly to the field of nondestructive testing.

Professional Experience:

Dr. Zisheng Guo currently serves as a faculty member and researcher at Beijing University of Technology. He has accumulated extensive experience in both academia and industry. As a leading researcher in electromagnetic technology, Dr. Guo has been involved in various collaborative research projects, bridging the gap between theoretical principles and real-world applications. His work on magnetic field sensing has been pivotal in solving industry-specific problems, particularly in the nondestructive testing of industrial materials such as steel pipes. Over the years, he has worked with industry partners to refine his detection systems, focusing on steel pipe integrity monitoring, ensuring safety in engineering systems. His teaching experience includes guiding graduate students and contributing to the development of new courses related to electromagnetic theory and sensor technologies.

Research Interests:

Dr. Zisheng Guo’s research interests are centered on electromagnetic sensing, specifically in the use of harmonic magnetic fields and targeted magnetic moments for material defect detection. His work has contributed significantly to the development of targeted magnetic field focusing techniques and spatial distance tracking algorithms to enhance detection systems. His primary research focus is on improving the detection of defects in steel pipes, particularly under extreme conditions such as deep burial or thick cladding. His innovative approaches, including a novel harmonic inner product correlation layer for data matching, aim to address challenges in achieving high detection clarity and accuracy. Dr. Guo is also interested in the broader applications of magnetic sensing in infrastructure and industrial safety, seeking to contribute practical solutions for various engineering challenges.

Awards:

Dr. Zisheng Guo has received numerous accolades for his groundbreaking work in electromagnetic sensing and nondestructive testing technologies. His research on steel pipe defect detection has earned recognition in both academic and industrial circles, with multiple awards for innovation in sensor system design. Notably, his novel approach to enhancing detection height and clarity in extreme conditions has been lauded by engineering communities. Dr. Guo has been awarded prestigious academic honors, including funding for research projects and recognition for contributions to the development of advanced magnetic field detection methods. His research has also been published in leading journals, and he has been invited to present his findings at various international conferences, further solidifying his reputation as an expert in the field.

Publications:

A non-excavation detection method for buried PE pipelines based on 3D time-domain stacking focusing of elastic wave reflection
πŸ‘€ Y. Qi, X. Wang, L. Yang, Y. Wang, Z. Guo | πŸ“– Measurement Science and Technology | 🌍 2024 🌟

Haitao GE | Algorithm Development | Young Scientist Award

Assist Prof Dr. Haitao GE | Algorithm Development | Young Scientist Award

Assist Prof Dr. Haitao GE | Algorithm Development – Chang’an University, China

Prof. Dr. Haitao GE is an Assistant Professor at Chang’an University, China, specializing in transportation engineering. He holds a Ph.D. from the University of Strasbourg, France, awarded in 2023. Prof. GE is recognized for his expertise in pavement engineering, computational mechanics, and tire-pavement interaction. His research integrates multi-scale numerical simulations to address issues of asphalt pavement deterioration, with a focus on optimizing road surfaces amid growing traffic demands and aging infrastructure. He has led numerous key transportation research projects between China and France and has made significant contributions to both academic and industry sectors. Prof. GE has been a youth editorial board member of the Journal of Highways and Automotive Transport and has actively reviewed for over 10 journals and conferences. A multilingual scholar, he has delivered keynote talks at numerous international conferences and built strong collaborations with French transportation universities and research institutions.

Profile:

OrcidΒ |Β Scopus

Education:

Prof. Dr. Haitao GE earned his Ph.D. in Civil Engineering from the University of Strasbourg, France, in 2023. Prior to this, he completed his undergraduate and Master’s degrees in Civil Engineering at prominent institutions in China. During his doctoral studies, Prof. GE focused on the tire-pavement interaction system, utilizing both the Finite Element Method (FEM) and the Discrete Element Method (DEM) to study multi-scale interactions in pavement engineering. His doctoral research provided groundbreaking insights into pavement surface deterioration mechanisms and paved the way for enhanced asphalt pavement design. Prof. GE’s academic journey in France not only honed his technical skills but also enabled him to develop cross-cultural and international research collaborations, which are a hallmark of his current work. His commitment to advancing the field of transportation engineering is reflected in his innovative approach to addressing complex infrastructure challenges, both in academia and industry.

Professional Experience:

Prof. Dr. Haitao GE is an Assistant Professor at Chang’an University, China, where he applies his extensive research experience to both teaching and academic projects. His professional career spans a range of roles in both China and France, where he has contributed to key transportation research collaborations. Prof. GE has co-led over nine major international research projects involving China and France, focusing on advanced numerical simulations in pavement engineering and tire-pavement interaction. His role in leading these projects has reinforced his expertise in both computational mechanics and practical applications in transportation infrastructure. Additionally, Prof. GE has served as a reviewer for over 10 prestigious academic journals and conferences. His leadership in the field is further recognized by his appointment to the youth editorial board of the Journal of Highways and Automotive Transport. Prof. GE’s academic contributions extend beyond research, as he actively contributes to shaping the future of pavement engineering and transportation systems.

Research Interests:

Prof. Dr. Haitao GE’s primary research interests lie in pavement engineering, computational mechanics, and tire-pavement interaction. His work focuses on utilizing advanced numerical simulation techniques, particularly the Finite Element Method (FEM) and Discrete Element Method (DEM), to model and understand the complex interaction between tires and pavement surfaces. As traffic volume continues to rise globally, one of the main challenges in transportation infrastructure is the deterioration of asphalt pavements. Prof. GE’s research aims to address this issue by exploring the mechanisms behind pavement surface degradation and improving pavement design to optimize performance and longevity. By incorporating multi-scale simulation methods, he seeks to bridge the gap between theoretical models and practical applications in real-world road networks. His research plays a crucial role in ensuring that roads can withstand increasing traffic loads while maintaining cost-effective maintenance strategies, which is critical in today’s rapidly developing infrastructure systems.

Awards:

Prof. Dr. Haitao GE has received several prestigious awards throughout his academic career, highlighting his outstanding contributions to transportation engineering. He is the recipient of the 13th Abertis Prize, becoming the first Chinese scholar to receive this honor, which was awarded to him by former Spanish Prime Minister Elena Salgado. This achievement was also featured in a special feature by Changjiang Daily, a major national news outlet in China. Prof. GE’s remarkable research has also earned him recognition at multiple international academic conferences, where he has received five academic awards, underscoring his significant impact on the field of pavement engineering. Additionally, Prof. GE has been selected for China’s prestigious Overseas Talent Recruitment Program, further solidifying his reputation in the global research community. His success as a researcher is matched by his commitment to the advancement of knowledge, as demonstrated by his active participation in international collaborations and his extensive publication record.

Publications:

From macro to micro: investigation of three-dimensional particle-scale responses of asphalt mixtures under non-uniform rolling tire loads via coupled FEM-DEM simulations
πŸ‘€ Haitao Ge, Juan Carlos Quezada, Vincent Le Houerou, Cyrille Chazallon, Aimin Sha | πŸ“– Road Materials and Pavement Design | 🌍 2025 | March 18 | DOI: 10.1080/14680629.2025.2478617 πŸŒŸπŸŽ“πŸ’Ό

Investigation of Particle-Scale Responses of Asphalt Mixtures Under Non-uniform Rolling Tire Loads
πŸ‘€ Haitao Ge, Juan Carlos Quezada, Vincent Le Houerou, Cyrille Chazallon | πŸ“– Book Chapter | 🌍 2024 | DOI: 10.1007/978-3-031-67252-1_51 πŸŒŸπŸŽ“

A new tire-sensor-pavement coupling chain for investigating asphalt mixture responses under rolling tire loads
πŸ‘€ Haitao Ge, Juan Carlos Quezada, Vincent Le Houerou, Cyrille Chazallon, Pierre Hornych | πŸ“– Road Materials and Pavement Design | 🌍 2023 | April 25 | DOI: 10.1080/14680629.2023.2180833 πŸŒŸπŸŽ“πŸ’Ό

Three-dimensional FEM–DEM coupling simulation for analysis of asphalt mixture responses under rolling tire loads
πŸ‘€ Haitao Ge, Juan Carlos Quezada, Vincent Le Houerou, Cyrille Chazallon | πŸ“– Construction and Building Materials | 🌍 2023 | March | DOI: 10.1016/j.conbuildmat.2023.130615 πŸŒŸπŸŽ“πŸ’Ό

Three-Dimensional Fem-Dem Coupling Simulation for Analysis of Asphalt Mixture Responses Under Rolling Tire Loads
πŸ‘€ Haitao Ge, Cyrille Chazallon, Juan Carlos Quezada, Vincent Le Houerou | πŸ“– SSRN | 🌍 2022 | EID: 2-s2.0-85134217262 | ISSN: 15565068 πŸŒŸπŸŽ“

Multiscale analysis of tire and asphalt pavement interaction via coupling FEM–DEM simulation
πŸ‘€ Haitao Ge, Juan Carlos Quezada, Vincent Le Houerou, Cyrille Chazallon | πŸ“– Engineering Structures | 🌍 2022 | April | DOI: 10.1016/j.engstruct.2022.113925 πŸŒŸπŸŽ“πŸ’Ό

Amin Hekmatmanesh | Algorithm Development | Best Researcher Award

Dr. Amin Hekmatmanesh l Algorithm Development Β | Best Researcher Award

LUT University, Finland

Author Profile

Google Scholar

πŸ”Ž Summary

Dr. Amin Hekmatmanesh is a skilled biomedical engineer and data scientist, specializing in wearable sensors, AI, and machine learning applications for health technologies. He has extensive experience in biosignal processing (EEG, ECG, PPG, EMG, GSR, IMU), rehabilitation robotics, and medical device development. With a Ph.D. in Mechanical Engineering (Biomedical Engineering), he has made significant contributions to the field by developing real-time systems for rehabilitation and health monitoring. His research focuses on using AI to advance rehabilitation robotics and human-robot interactions, publishing over 30 peer-reviewed articles. He is also an active reviewer and editorial board member for several high-ranking journals.

πŸŽ“ Education

Dr. Hekmatmanesh holds a Ph.D. in Mechanical Engineering (Biomedical Engineering) from LUT University in Finland, where he achieved a GPA of 4.33. His dissertation focused on artificial intelligence and machine learning for EEG signal processing in rehabilitation robotics. He also completed his M.Sc. in Biomedical Engineering at Shahed University in Iran, with a perfect GPA of 4.0, and a B.Sc. in Electrical Engineering from Islamic Azad University, graduating with a GPA of 4.1.

πŸ’Ό Professional Experience

Dr. Hekmatmanesh has a diverse professional background, having worked as a Lead Project Manager at Mevea Company, where he managed health monitoring system projects, incorporating wearable sensor technology. At Flowgait Company, he contributed to mathematical solutions for horse motion analysis. As a Junior Researcher and Project Manager at LUT University, he focused on wearable sensor systems and health monitoring. Additionally, he worked as a Research Assistant at Tehran University, working on diagnostic algorithms and therapeutic systems in health technologies.

πŸ“š Academic Citations

Dr. Hekmatmanesh has published over 30 peer-reviewed papers, including book chapters and review articles, and his work has contributed significantly to the biomedical engineering and AI fields. His research has received wide recognition, and he is regularly invited to participate in scientific conferences and review for high-impact journals, showcasing his influence and contributions to advancing health technologies.

πŸ”§ Technical Skills

Dr. Hekmatmanesh is highly proficient in biosignal processing, including EEG, ECG, PPG, EMG, GSR, and IMU signals. He is skilled in machine learning and AI, specifically for biosignal classification and real-time system development. His technical expertise extends to embedded electronics, sensor design, and circuit board assembly. He is proficient in Python and Matlab for data analysis and has experience using version control tools like GitHub to manage research projects.

πŸŽ“ Teaching Experience

Dr. Hekmatmanesh has been actively involved in teaching and mentoring within the biomedical engineering field. He has supervised PhD students and contributed to academic programs by delivering lectures and guiding research projects in health technologies, machine learning, and rehabilitation robotics.

πŸ”¬ Research Interests On Algorithm Development

Dr. Hekmatmanesh’s research interests include the development of wearable sensor systems for health monitoring, AI and machine learning techniques for biosignal classification, and the design and control of rehabilitation robotics. He is also focused on human-robot interactions and improving prosthetics control through innovative AI applications in healthcare.

πŸ“– Top Noted Publications

Nanocaged platforms: modification, drug delivery and nanotoxicity. Opening synthetic cages to release the tiger
    • Authors: PS Zangabad, M Karimi, F Mehdizadeh, H Malekzad, A Ghasemi, …
    • Journal: Nanoscale
    • Year: 2017
Review of the state-of-the-art of brain-controlled vehicles
    • Authors: A Hekmatmanesh, PHJ Nardelli, H Handroos
    • Journal: IEEE Access
    • Year: 2021
Neurosciences and wireless networks: The potential of brain-type communications and their applications
    • Authors: RC Moioli, PHJ Nardelli, MT Barros, W Saad, A Hekmatmanesh, …
    • Journal: IEEE Communications Surveys & Tutorials
    • Year: 2021
A combination of CSP-based method with soft margin SVM classifier and generalized RBF kernel for imagery-based brain computer interface applications
    • Authors: A Hekmatmanesh, H Wu, F Jamaloo, M Li, H Handroos
    • Journal: Multimedia Tools and Applications
    • Year: 2020
EEG control of a bionic hand with imagination based on chaotic approximation of largest Lyapunov exponent: A single trial BCI application study
    • Authors: A Hekmatmanesh, RM Asl, H Wu, H Handroos
    • Journal: IEEE Access
    • Year: 2019

Shiming Li | Algorithm Development | Best Researcher Award

Dr. Shiming Li l Algorithm DevelopmentΒ | Best Researcher Award

North China University of Water Resources and Electric Power, China

Author Profile

Orcid

πŸŽ“ Early Academic Pursuits

Dr. Shiming Li began his academic journey with a strong foundation in Surveying and Mapping Science and Technology. He earned his Ph.D. in 2021 from Southwest Jiaotong University, Chengdu, China, where his research laid the groundwork for innovations in geospatial data integration and 3D modeling. His early interest in integrating diverse data sources shaped his career trajectory, leading to groundbreaking studies in multi-type point cloud data fusion.

πŸ’Ό Professional Endeavors

Currently, Dr. Li serves as a Lecturer and Master Tutor at the College of Surveying and Geo-informatics, North China University of Water Resources and Electric Power, Zhengzhou, China. In this capacity, he mentors aspiring professionals and collaborates on projects aimed at enhancing urban and water environment monitoring using advanced geoinformatics tools.

🌍 Contributions and Research Focus

Dr. Li’s research revolves around the fusion of multi-type point cloud data obtained from drones, vehicles, backpacks, and handheld devices. His work addresses critical challenges in three-dimensional reconstruction of complex structures and intelligent scene recognition. By leveraging cutting-edge techniques, he has contributed to monitoring urban and aquatic environments, enabling more efficient and accurate spatial analysis.

🌟 Impact and Influence

Dr. Li’s innovative research has advanced the field of surveying and mapping, particularly in urban planning and environmental monitoring. His studies have practical applications in designing sustainable infrastructure and improving disaster management strategies. His influence extends beyond academia, benefiting industries focused on spatial data and urban development.

πŸ“š Academic Contributions

Dr. Li has authored and contributed to several peer-reviewed articles and conference papers, earning citations in prominent journals. His work is widely recognized for its technical rigor and relevance, making significant strides in geoinformatics research.

πŸ› οΈ Technical Skills

Dr. Li is proficient in cutting-edge geospatial technologies and software for data integration and analysis, including GIS tools, point cloud processing platforms, and 3D modeling frameworks. His technical expertise supports his research in creating precise and scalable solutions for complex spatial challenges.

πŸ§‘β€πŸ« Teaching Experience

As a lecturer, Dr. Li is dedicated to shaping the next generation of geoinformatics professionals. He combines theory with practical applications in his teaching, inspiring students to explore innovative solutions in surveying and mapping. His mentorship extends to graduate students, helping them tackle advanced research problems.

πŸš€ Legacy and Future Contributions

Dr. Shiming Li is poised to leave a lasting legacy in the field of surveying and geoinformatics. His future endeavors aim to further enhance the integration of multi-source spatial data for intelligent scene recognition and complex structural reconstruction. By continuing to push the boundaries of geospatial research, he is set to contribute to the sustainable development of urban and water environments globally.

πŸ“– Top Noted Publications

Object-Level Contrastive-Learning-Based Multi-Branch Network for Building Change Detection from Bi-Temporal Remote Sensing Images
    • Authors: Shiming Li, Fengtao Yan, Cheng Liao, Qingfeng Hu, Kaifeng Ma, Wei Wang, Hui Zhang
    • Journal: Remote Sensing
    • Year: 2025
Optimal Feature-Guided Position-Shape Dual Optimization for Building Point Cloud Facade Detail Enhancement
    • Authors: Shiming Li, Fengtao Yan, Kaifeng Ma, Qingfeng Hu, Feng Wang, Wenkai Liu
    • Journal: Remote Sensing
    • Year: 2024
Study on the Influence of Label Image Accuracy on the Performance of Concrete Crack Segmentation Network Models
    • Authors: Kaifeng Ma, Mengshu Hao, Wenlong Shang, Jinping Liu, Junzhen Meng, Qingfeng Hu, Peipei He, Shiming Li
    • Journal: Sensors
    • Year: 2024
OFFS-Net: Optimal Feature Fusion-Based Spectral Information Network for Airborne Point Cloud Classification
    • Authors: Peipei He, Kejia Gao, Wenkai Liu, Wei Song, Qingfeng Hu, Xingxing Cheng, Shiming Li
    • Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
    • Year: 2023
Spatiotemporal Information Mining for Emergency Response of Urban Flood Based on Social Media and Remote Sensing Data
    • Authors: Hui Zhang, Hao Jia, Wenkai Liu, Junhao Wang, Dehe Xu, Shiming Li, Xianlin Liu
    • Journal: Remote Sensing
    • Year: 2023
TerraSAR-X and GNSS Data for Deformation Detection and Mechanism Analysis of a Deep Excavation Channel Section of the China South–North Water-Diversion Project
    • Authors: Qingfeng Hu, Yingchao Kou, Jinping Liu, Wenkai Liu, Jiuyuan Yang, Shiming Li, Peipei He, Xianlin Liu, Kaifeng Ma, Yifan Li, et al.
    • Journal: Remote Sensing
    • Year: 2023
Multilevel Feature Aggregated Network with Instance Contrastive Learning Constraint for Building Extraction
    • Authors: Li Shiming, Tingrui Bao, Hui Liu, Rongxin Deng, Hui Zhang
    • Journal: Remote Sensing
    • Year: 2023

Yanjie Zhu | Algorithm Development | Best Researcher Award

Prof. Yanjie Zhu | Algorithm Development | Best Researcher Award

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

πŸ‘¨β€πŸŽ“Professional Profiles

πŸ”¬ 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.

πŸŽ“ 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.

πŸ“š 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).

πŸ” 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.

πŸ’» 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.

πŸ‘¨β€πŸ« 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.

πŸ“–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

Shubing Dai | Algorithm Development | Best Researcher Award

Prof. Shubing Dai | Algorithm Development | Best Researcher Award

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

Professional ProfileπŸ‘¨β€πŸŽ“

πŸ‘¨β€πŸ« 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).

πŸŽ“ 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 CoruΓ±a in Spain (2024).

πŸ’Ό 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.

πŸ“š 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.

πŸ”§ 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.

πŸ‘©β€πŸ« 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.

πŸ”¬ 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.

πŸ” 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.

 

πŸ“–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