Mr. Gang Wei | Image Recognition | Best Researcher Award

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Mr. Gang Wei | Image Recognition | Best Researcher Award

Mr. Gang Wei, Tongji University, China

Mr. Gang Wei is an accomplished researcher specializing in Computer Graphics, Geographic Information Systems (GIS), and Building Information Modeling (BIM). He holds a Ph.D. in Computer Application Technology from Tongji University, Shanghai, focusing on digital city visualization. With over two decades of experience at the CAD Research Center, Tongji University, he has significantly contributed to advancements in computer-aided design (CAD), artificial intelligence, and image recognition. His research explores 3D modeling, graphical interaction, and level-of-detail techniques for smart city applications. Mr. Wei’s expertise in integrating AI with GIS and BIM has led to innovative solutions for urban planning and digital infrastructure. His groundbreaking work continues to shape the future of computational design and intelligent visualization technologies.

šŸŒĀ Professional ProfileĀ 

Scopus

šŸ† Suitability for Best Researcher Award

Mr. Gang Weiā€™s extensive contributions to computer graphics, GIS, and AI-driven visualization make him an excellent candidate for the Best Researcher Award. His pioneering work in digital city modeling, 3D visualization, and feature-based modeling has advanced computational methods for urban development. With over 20 years of research experience, he has played a crucial role in integrating AI-driven image recognition into GIS and CAD applications. His research enhances urban planning efficiency and digital infrastructure visualization, making him a leading figure in smart city development. Recognized for his expertise in building information modeling and computational graphics, Mr. Weiā€™s work aligns with cutting-edge technological advancements, making him a deserving recipient of this prestigious award.

šŸŽ“ EducationĀ 

Mr. Gang Wei completed his Ph.D. in Computer Application Technology at Tongji University, Shanghai (2008). His doctoral research focused on key technologies for digital city visualization, integrating GIS and 3D modeling to enhance urban digitalization. Prior to this, he earned a Masterā€™s degree (2000) in Computer Application Technology from the same university, specializing in 3D solid modeling and graphical user interface design. His academic training has equipped him with expertise in computer-aided design (CAD), computer graphics, and feature-based modeling. His educational background provided a strong foundation for his contributions to AI-driven urban simulation, visualization technologies, and digital infrastructure modeling, making him a leader in computational design and geographic information systems.

šŸ’¼ ExperienceĀ 

Mr. Gang Wei has been an Associate Research Fellow at the CAD Research Center, Tongji University, Shanghai, since 2000. His career focuses on computer graphics, AI-driven GIS applications, and CAD-based modeling. He has led research on digital city visualization, AI-driven image recognition, and building information modeling (BIM), significantly impacting smart city development. His expertise in 3D visualization, level-of-detail modeling, and graphical interactions has improved digital infrastructure design and urban planning. Over two decades, he has collaborated on projects integrating AI with geographic data systems, enhancing real-time urban simulations. His work bridges AI, spatial data analytics, and computational design, contributing to technological innovations in urban digitalization and 3D city modeling.

šŸ”¬ Research FocusĀ 

Mr. Gang Weiā€™s research focuses on AI-driven computer graphics, image recognition, and GIS-based digital city modeling. His work integrates machine learning with 3D visualization, improving real-time urban simulations. He specializes in feature-based modeling, level-of-detail (LoD) techniques, and CAD applications, enhancing smart city development. His expertise in AI-driven BIM has revolutionized building data management and infrastructure planning. He explores graphical interaction methods and spatial data integration, improving geospatial analytics. His research also includes automated 3D city reconstruction and real-time visualization algorithms, optimizing digital urban planning. Through AI-enhanced computational design and GIS modeling, Mr. Weiā€™s innovations contribute to smarter, more efficient urban digitalization and intelligent geospatial data analysis.

šŸ“ŠĀ Publication Top Note

AttenPoint: Exploring Point Cloud Segmentation Through Attention-Based Modules

 

Samsil Arefin Mozumder | Machine Vision | Best Researcher Award

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Samsil Arefin Mozumder | Machine Vision | Best Researcher Award

Mr. Samsil Arefin Mozumder, China University of Mining and Technology, China.

Samsil Arefin Mozumder is a dedicated Master’s student at China University of Mining and Technology (CUMT) in Xuzhou, China, specializing in mechanical engineering, embedded machine learning, and robotics. His research focuses on IoT, machine vision, and sensor integration for industrial applications such as derailment detection and mining infrastructure anomaly detection. With a strong background in electronics and embedded systems, Samsil has contributed to multiple publications and is actively involved in projects enhancing intelligent systems for automation. He is passionate about advancing technology for real-world impactĀ šŸŒšŸ”§šŸ¤–.

Publication Profile

Orcid
Googlescholar

Education & Experience:

  • China University of Mining and Technology (CUMT), 2022ā€“2025 (CGPA: 4.5/5)
    šŸŽ“Ā Bachelor of Science in Electrical and Electronic Engineering
  • East Delta University (EDU), Bangladesh, 2018ā€“2020 (CGPA: 3.26/4)
    šŸŽ“Ā Diploma in Electronics Technology
  • Chattogram Polytechnic Institute (CPI), Bangladesh, 2013ā€“2017 (CGPA: 3.12/4)
    šŸ’¼Ā Research Assistant
  • China University of Mining and Technology (CUMT), 2022ā€“Present
    šŸ’¼Ā Deputy Field Controller
  • OPSEED CO., (BD) LTD, 2020ā€“2021

Suitability for the Award

Mr. Samsil Arefin Mozumder is an excellent candidate for the Best Researcher Award, with a strong academic background and impressive research contributions in electronics, embedded machine learning, IoT, and robotics. Currently pursuing a Master’s in Mechanical Engineering at China University of Mining and Technology, he has published impactful work on intelligent systems, such as mining derailment detection and IoT-based water management. His innovative projects, including machine vision applications and autonomous robots, showcase his ability to tackle complex real-world challenges. Samsil’s interdisciplinary approach and commitment to advancing technology make him a standout researcher deserving of this prestigious recognition.

Professional Development

Samsil Arefin Mozumder’s professional development is rooted in a strong commitment to expanding his expertise in mechanical engineering, electronics, and machine learningĀ šŸ¤–. He has been involved in several cutting-edge research projects, such as derailment detection using machine vision on edge devices and smart IoT systems. His industry experience as a deputy field controller sharpened his problem-solving and project management skillsĀ šŸ› ļø. Through his research assistantship at CUMT, he has further honed his skills in robotics, IoT, and sensor integration, contributing to publications in top conferences and journalsĀ šŸ“š.

Research Focus

Samsil Arefin Mozumder’s research focus is at the intersection ofĀ electronics, machine learning (ML), IoT, andĀ roboticsĀ šŸ”§šŸ¤–. He investigates embedded ML systems, edge AI, and computer vision for real-time anomaly detection and intelligent automationĀ šŸ§ . His work includes developing smart systems for mining infrastructure, including derailment detection, vertical shaft detection, and IoT-based solutions for environmental monitoringĀ šŸŒ±. With a passion for exploring innovative solutions in industrial automation and machine vision, Samsil aims to bridge the gap between cutting-edge technologies and practical, impactful applicationsĀ šŸŒšŸ“”.

Publication Highlights

  • Smart IoT-Biofloc water management system using Decision regression tree,Ā 2021Ā šŸ“˜šŸŒŠĀ –Ā Cited by 12
  • Research on Vertical Shaft Detection System Based on CMOS-Camera and Lidar,Ā IEEE Access, 2024Ā šŸ“˜šŸ”
  • Derailment Detection of Mining Shaft’s Rail Vehicle Using Machine Vision on Edge Device, 2024Ā šŸ“…šŸš‚
  • IRHA: An Intelligent RSSI based Home automation System, Ā 2022Ā šŸ“˜šŸ 

Dr. Jiangning Zhang | Computer Vision | Best Researcher Award

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Dr. Jiangning Zhang | Computer Vision | Best Researcher Award

Dr. Jiangning Zhang, Zhejiang University, China

Dr. Jiangning Zhang, a Principal Researcher at YouTu Lab, Tencent in Shanghai, holds a Ph.D. in Control Science and Engineering and an M.D. from Zhejiang University, as well as a B.S. in Electronic Information from Wuhan University. He leads the Industry Perception and AIGC teams, focusing on neural architecture design, particularly transformer-based and lightweight vision models. Dr. Zhang’s research spans multi-modal AIGC, including image and video generation, human-centric editing, and virtual digital human technologies, and extends to 3D scene segmentation with foundation models for visual anomaly classification. His work drives advancements in cutting-edge technologies and applications in artificial intelligence and computer vision.

Professional Profile:

Google Scholar

Suitability for the Award

Dr. Jiangning Zhang is highly suitable for the Best Researcher Award for the following reasons:

  1. Innovative Research Contributions:
    • Dr. Zhangā€™s work in neural architecture design and multi-modal generative models has advanced the field of computer vision and AI. His research on transformer-based architectures and lightweight vision models contributes to the efficiency and effectiveness of AI systems.
    • His involvement in developing multi-modal GANs, VAEs, and diffusion models for various applications, including 2D/3D digital human generation and motion generation, demonstrates significant innovation and impact in the field.
  2. High-Impact Publications:
    • Dr. Zhang has published influential papers in top-tier conferences such as ICCV and TIP, showcasing his contributions to cutting-edge research. His work on efficient attention-based models and anomaly detection reflects his expertise and impact on the academic community.
  3. Leadership and Influence:
    • As a Principal Researcher at Tencentā€™s YouTu Lab, Dr. Zhang leads teams working on industry perception and advanced AI-generated content, indicating his role in shaping future technologies and influencing both academic and industrial research.
  4. Relevance and Application:
    • Dr. Zhangā€™s research addresses practical and emerging challenges in AI, such as image/video generation, digital human reconstruction, and scene segmentation. His work has practical applications in various industries, enhancing its relevance and impact.

Educational BackgroundĀ 

Dr. Zhang earned his Ph.D. in Control Science and Engineering from Zhejiang University, Hangzhou, China, under the guidance of Prof. Yong Liu. He pursued his M.D. at the same institution and obtained his B.S. in Electronic Information from Wuhan University.

Current Position

Dr. Jiangning Zhang is a Principal Researcher leading the Industry Perception and AIGC teams at YouTu Lab, Tencent, Shanghai.

Research InterestsĀ 

  • Neural Architecture Design: Specializing in transformer-based architectures and lightweight vision models.
  • Multi-modal AIGC Research: Investigating image and video generation, human-centric editing, and 2D/3D virtual digital human technologies, including reconstruction and animation.
  • 3D Scene Segmentation: Utilizing foundation models for visual anomaly classification and segmentation.

Publication Top Notes:

  • Title: Omni-Frequency Channel-Selection Representations for Unsupervised Anomaly Detection
    • Year: 2023
    • Cited by: 96
  • Title: Rethinking Mobile Block for Efficient Attention-Based Models
    • Year: 2023
    • Cited by: 78
  • Title: Towards Open Vocabulary Learning: A Survey
    • Year: 2024
    • Cited by: 71
  • Title: Multimodal Industrial Anomaly Detection via Hybrid Fusion
    • Year: 2023
    • Cited by: 64
  • Title: Region-Aware Face Swapping
    • Year: 2022
    • Cited by: 56

 

 

Mr. Xiaoyin Zheng | Computer Vision Awards | Best Researcher Award

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Mr. Xiaoyin Zheng | Computer Vision Awards | Best Researcher Award

Mr. Xiaoyin Zheng, XMotors.ai, United States

Mr. Xiaoyin Zheng is a skilled Computer Vision Algorithm Engineer at XMotors.ai, where he focuses on integrating deep learning for advanced cabin monitoring and driver state analysis. With an M.S. in Engineering Technology from Purdue University and a Bachelor’s in Automotive Engineering from Wuhan University of Technology, Xiaoyin excels in Python, C, C++, and MATLAB/Simulink. His technical expertise encompasses computer vision and deep learning, particularly in object classification, detection, and tracking. Xiaoyinā€™s notable research includes autonomous vehicle simulators and lithium battery estimation, and he has achieved recognition with a first prize at the GM Tech Center competition. Additionally, his experience as a Graduate Teaching Assistant at Purdue University underscores his dedication to advancing engineering education and technology.

šŸŒ Professional Profile:
Google Scholar

šŸŽ“ Education:

Xiaoyin Zheng earned his M.S. in Engineering Technology from Purdue University, specializing in robotics and self-driving technology. He also holds a Bachelor’s in Automotive Engineering from Wuhan University of Technology.

šŸ”¬ Technical Skills:

Xiaoyin is proficient in Python, C, C++, and MATLAB/Simulink. His expertise includes computer vision and deep learning, with a focus on object classification, detection, segmentation, tracking, and model acceleration.

šŸ’¼ Professional Experience:

Xiaoyin is currently a Computer Vision Algorithm Engineer at XMotors.ai, where he integrates deep learning into cabin monitoring systems and improves driver state monitoring through advanced data processing. Previously, he interned as a Mechanical Engineer at Along Aircraft Manufacturing Company, where he worked on airplane mechanical parts and fly test approvals.

šŸ”¬ Research Experience:

His research includes developing simulators for autonomous vehicle dynamics and lithium battery state-of-charge estimation using extended Kalman filtering. He also contributed to designing a foldable personal mobility device, winning first prize at the GM Tech Center competition.

šŸ“š Teaching Experience:

As a Graduate Teaching Assistant at Purdue University, Xiaoyin taught Automated Manufacturing Processes and Applied Statics, guiding students in CNC operations, CAD design, and fundamental engineering concepts.

šŸ† Achievements:

Xiaoyin’s innovative work in robotics and autonomous systems, coupled with his successful research and teaching roles, highlights his commitment to advancing engineering technology and education.

Publication Top Notes:

Lithium battery soc estimation based on extended kalman filtering algorithm
  • Year: 2018
  • Citations: 27
Multi-scale fractal characteristics of the pore system in low-permeability conglomerates from the junggar basin
  • Year: 2023
  • Citations: 2
Anything in Any Scene: Photorealistic Video Object Insertion
  • Year: 2024
A Minimal Set of Parameters Based Depth-Dependent Distortion Model and Its Calibration Method for Stereo Vision Systems
  • Year: 2024