Xia Renbo | Robotic Vision | Best Researcher Award

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Mr.Xia Renbo | Robotic Vision | Best Researcher Award

Researcher, Shenyang Institute of Automation and Chinese Academy of Sciences, China

Dr. Xia Renbo is a distinguished researcher and doctoral supervisor at the Shenyang Institute of Automation, Chinese Academy of Sciences (CAS) 🧠🤖. With a Ph.D. in Engineering from CAS and degrees from Harbin Institute of Technology 🎓, Dr. Xia specializes in industrial optical measurement, robotic vision, and intelligent manufacturing 🔬📸. He has led innovative projects in 3D reconstruction, machine learning, and pattern recognition 🛠️💡. A key contributor to smart industry technologies, he earned recognition with the Liaoning Provincial Science and Technology Progress Award 🏅. His work bridges advanced computer vision and real-world automation challenges .

 Profile

🔹 Education & Experience :

Dr. Xia Renbo earned his 🎓 Ph.D. in Engineering in 2006 from the Shenyang Institute of Automation, Chinese Academy of Sciences (CAS), where he specialized in 3D reconstruction for industrial applications. He also holds an 🎓 M.S. (2002) and 🎓 B.S. (2000) in Mechanical Engineering and Automation from Harbin Institute of Technology. His professional journey began as an 👨‍🔬 Assistant Researcher (2006–2008) at SIA, CAS, where he developed algorithms for photogrammetry and surface reconstruction. He then served as an 👨‍🔬 Associate Researcher (2009–2018), focusing on 3D vision, defect detection, and camera calibration. Since 2019, he has been a leading 👨‍🔬 Researcher at SIA, driving projects in intelligent optical measurement and robotic vision systems.

📚 Professional Development :

Dr. Xia Renbo has steadily advanced his career in industrial automation and intelligent systems 🔧🤖. Beginning as an Assistant Researcher, he contributed to early developments in 3D surface reconstruction and photogrammetry 📐📷. As an Associate Researcher, he expanded into multi-camera calibration and defect detection, contributing to industry-grade systems for quality assurance and control 🛠️🧪. Now a lead Researcher, he spearheads high-impact projects in intelligent measurement and robotic vision, applying computer vision and AI to automation tasks 🤖🔍. His leadership reflects a commitment to integrating smart technologies into real-world industrial environments ⚙️🌐.

🔬 Research Focus :

Dr. Xia’s research spans several interconnected domains at the intersection of automation and intelligence 🧠⚙️. He focuses on industrial optical measurement, advancing precision in manufacturing with 3D reconstruction and dynamic tracking technologies 📏🔬. His work in robotic vision and intelligent manufacturing leverages machine learning, computer vision, and pattern recognition to improve industrial adaptability and efficiency 🤖📸. By merging hardware integration with software intelligence, he contributes to the evolution of Industry 4.0 applications 🚀🏭. His research enhances robotic equipment with real-time perception and adaptability, fostering smarter production lines and inspection systems 🛠️📊.

🏆 Awards and Honors :

Dr. Xia Renbo was honored with the 🥉 Third Prize of the Liaoning Provincial Science and Technology Progress Award in 2011. This recognition was awarded for his outstanding contribution to the development of a 3D Photogrammetric System designed for accurate railway tanker volume measurement 🔍🚆. The project showcased his expertise in applying advanced optical measurement techniques to solve complex industrial challenges, further establishing his reputation in the field of intelligent manufacturing and robotic vision 🤖📐

Publication Top Notes :

A Spectral-Domain Low-Coherence Method for Measuring Composite Windshield Thickness

Citation:
Tao Zhang, Renbo Xia, Jibin Zhao, Yanyi Sun, Jiajun Wu, ShengPeng Fu, Yueling Chen.
“A Spectral-Domain Low-Coherence Method for Measuring Composite Windshield Thickness.” IEEE Transactions on Instrumentation and Measurement, 2024.
DOI: 10.1109/TIM.2024.3353865

Summary:
This paper presents a spectral-domain low-coherence interferometry method tailored for non-destructive and high-precision thickness measurement of composite windshields. The proposed technique compensates for multi-layer reflections and surface curvatures, enabling accurate measurements across curved, layered glass structures commonly used in automotive windshields. The method demonstrates enhanced reliability and resolution compared to traditional time-domain approaches, making it suitable for quality control in automotive manufacturing.

Robust Correspondences with Saliency for Point Cloud Registration

Citation:
Yinghao Li, Renbo Xia, Jibin Zhao, Junlan Yi, Taiwen Qiu.
“Robust Correspondences with Saliency for Point Cloud Registration.” Proceedings of the 2024 ACM International Conference on Graphics and Interaction, April 26, 2024.
DOI: 10.1145/3671151.3671191

Summary:
The authors propose a saliency-guided framework for robust point cloud registration. By integrating geometric saliency and feature consistency, the approach significantly improves correspondence accuracy, especially in scenes with partial overlap or heavy noise. Experimental results confirm superior performance compared to traditional methods like ICP and FGR, particularly in challenging real-world 3D environments such as indoor mapping and robotic navigation.

Low-Coherence Measurement Methods for Industrial Parts With Large Surface Reflectance Variations

Citation:
Tao Zhang, Renbo Xia, Jibin Zhao, Jiajun Wu, Shengpeng Fu, Yueling Chen, Yanyi Sun.
“Low-Coherence Measurement Methods for Industrial Parts With Large Surface Reflectance Variations.” IEEE Transactions on Instrumentation and Measurement, 2023.
DOI: 10.1109/TIM.2023.3301894

Summary:
This study develops a low-coherence interferometric system optimized for measuring the thickness of industrial parts with complex surfaces and high reflectance variability. The methodology integrates reflectance compensation and real-time spectral analysis, enabling high-resolution and repeatable measurements on metal, glass, and composite surfaces. The approach is validated across various industrial use cases including machined parts and reflective coatings.

Research on Optimization of Multi-Camera Placement Based on Environment Model

Citation:
Liming Tao, Renbo Xia, Jibin Zhao, Fangyuan Wang, Shengpeng Fu.
“Research on Optimization of Multi-Camera Placement Based on Environment Model.” Proceedings of the 2023 ACM International Conference on Intelligent Systems and Smart Environments, September 15, 2023.
DOI: 10.1145/3629264.3629288

Summary:
This paper introduces an optimization strategy for multi-camera placement in intelligent monitoring environments. Using a 3D environmental model, the proposed system maximizes surveillance coverage and minimizes blind spots by leveraging visibility analysis and coverage redundancy metrics. The algorithm proves effective in simulation and real-world testing, demonstrating practical value in smart buildings and industrial automation setups.

A High-Accuracy Circular Hole Measurement Method Based on Multi-Camera System

Citation:
Liming Tao, Renbo Xia, Jibin Zhao, Tao Zhang, Yinghao Li, Yueling Chen, Shengpeng Fu.
“A High-Accuracy Circular Hole Measurement Method Based on Multi-Camera System.” Measurement, Volume 205, February 2023, Article 112361.
DOI: 10.1016/j.measurement.2022.112361

Summary:
This work presents a multi-camera 3D reconstruction system for precise circular hole measurements in industrial components. The method employs stereo calibration, edge detection, and ellipse fitting techniques to extract geometric parameters with high accuracy. The system’s performance is validated against traditional single-camera and manual measurement approaches, achieving sub-millimeter precision and improved automation suitability.

Conclusion:

Dr. Xia Renbo exemplifies the qualities of a leading researcher—technical depth, cross-disciplinary innovation, real-world impact, and academic mentorship. His groundbreaking work continues to shape the future of intelligent manufacturing and robotic automation. In light of his achievements and contributions, he is a compelling and deserving recipient of the Best Researcher Award.

Mr. Rishik Gupta | Computer Vision | Best Researcher Award

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Mr. Rishik Gupta | Computer Vision | Best Researcher Award

Mr. Rishik Gupta, Texas A&M University, United States

Mr. Rishik Gupta is an emerging talent in the field of Computer Science, currently pursuing his Master’s degree at Texas A&M University, USA. With a strong foundation built at Maharaja Surajmal Institute of Technology and the Indian Institute of Technology Madras, he has shown exceptional promise in machine learning, natural language processing, computer vision, and audio signal processing. His professional experience includes impactful roles at the Defence Research and Development Organization (DRDO), AI Shala Technologies, and Growna EdTech, where he demonstrated his ability to develop high-performance AI systems. Rishik has authored research papers, developed NLP models with over 95% accuracy, and created scalable software solutions. His academic journey is marked by dedication, innovation, and cross-disciplinary collaboration. 🚀📚💡

🌍 Professional Profile 

Orcid

Google Scholar

🏆 Suitability for Best Researcher Award 

Mr. Rishik Gupta is highly deserving of the Best Researcher Award due to his outstanding contributions to applied machine learning, natural language processing, and intelligent systems. His work at DRDO led to the development of high-accuracy traffic classification models, while at AI Shala, he designed an NLP model achieving 95%+ accuracy in distinguishing AI-generated text. Rishik demonstrates not only technical skill but innovation and academic rigor, reflected in his publications and custom dataset designs. He bridges academia and industry with real-world applications and research, and his custom GPT model and smart attendance system further showcase his creativity and problem-solving ability. Rishik represents the next generation of researchers pushing the frontier of AI and computer science. 🧠🏅📈

🎓 Education 

Mr. Rishik Gupta is currently enrolled in the Master of Computer Science program at Texas A&M University (Aug 2024 – May 2026), where he continues to deepen his expertise in artificial intelligence and software systems. He completed his Bachelor of Technology in Computer Science and Engineering from Maharaja Surajmal Institute of Technology, Delhi (2020–2024). Simultaneously, he studied at the Indian Institute of Technology Madras from Sep 2021 to Dec 2023, gaining exposure to advanced courses and research environments. His academic journey reflects a strategic blend of technical depth, cross-institutional learning, and interdisciplinary exploration in AI, machine learning, and computer vision. 🎓🧑‍💻📖

💼 Experience 

Rishik has amassed hands-on research and development experience across prominent organizations. At DRDO, he built advanced machine learning models for network traffic classification, collaborating with senior scientists to improve accuracy and efficiency. At AI Shala Technologies, he designed an innovative NLP model capable of detecting AI-generated content, integrating BERT and perplexity-based analysis. His tenure at Growna EdTech showcased his software engineering skills, where he developed a scalable Android application with significant business impact. Each role highlights his interdisciplinary talent in ML, NLP, software development, and project execution, bridging theoretical knowledge with practical application. 🧑‍🔬💻🤝

🏅 Awards and Honors 

While still early in his academic and professional career, Rishik has been recognized for his high-impact work through collaborative research publications, top internship selections, and notable project contributions. His model at DRDO surpassed standard benchmarks with over 90% accuracy, and his AI Shala project achieved 95% accuracy, both earning internal commendation. His software at Growna EdTech played a pivotal role in securing a major client, boosting revenue by 60%, a rare feat for an intern-led project. His academic excellence has also earned him admission to the prestigious Texas A&M University and IIT Madras programs. More accolades are expected as his promising career progresses. 🥇🏆📜

🔬 Research Focus

Mr. Gupta’s research is focused on the intersection of Machine Learning, Efficient Search & Retrieval, Natural Language Processing, Computer Vision, and Audio Signal Processing. His work involves both theoretical exploration and real-world implementation of AI systems, including generative models, transformer architectures, semantic analysis, and facial recognition systems. He emphasizes the creation of scalable, high-performance solutions such as smart attendance tracking using facial recognition and custom GPT-style language models. His interest in audio signal processing and text classification expands his multidisciplinary relevance, while his projects reflect innovation, practical utility, and algorithmic efficiency. He seeks to create AI tools that are impactful, interpretable, and adaptable to varied use cases. 🤖📡🗣️📷🎶

📊 Publication Top Notes

  • ASKSQL: Enabling Cost-Effective Natural Language to SQL Conversion for Enhanced Analytics and Search

    • Year: 2025

  • Integrated Smart Attendance Tracker Using YOLOv8 and FaceNet with Spotify ANNOY

    • Year: 2024

  • Pronunciation Scoring With Goodness of Pronunciation and Dynamic Time Warping

    • Year: 2023

  • SwinAnomaly: Real-Time Video Anomaly Detection Using Video Swin Transformer and SORT

    • Year: 2023

 

 

Dr. Bo Wang | Image Computing | Best Researcher Award

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Dr. Bo Wang | Image Computing | Best Researcher Award

Dr. Bo Wang, Guangdong Polytechnic of Science and Technology, China

Dr. Bo Wang is an Associate Professor specializing in artificial intelligence and electronic information technology. He earned his Ph.D. in Computer Application Technology from Harbin Engineering University in 2012 and has since built a distinguished career in academia and research. Dr. Wang has held key academic positions at Guangdong Polytechnic of Science and Technology and Harbin University of Science and Technology. His research spans artificial intelligence, electronic information systems, and molecular imaging. A prolific researcher, he has led numerous funded projects and published extensively in high-impact journals. His work has significantly contributed to advancing intelligent systems and innovative applications, earning him recognition as a leading expert in his field. 🌟

Professional Profile:

Scopus
Orcid

Suitability for the Award

Dr. Wang’s exceptional contributions to artificial intelligence and electronic information technology make him a strong candidate for the Best Researcher Award. His leadership in funded research projects, including those in Guangdong Province and Heilongjiang, demonstrates his ability to address critical technological challenges. His interdisciplinary expertise and impactful publications have advanced the fields of AI and molecular imaging. Dr. Wang’s innovative work exemplifies excellence in research and reflects the award’s criteria for groundbreaking contributions to science and technology. 🏅

Education

🎓 Dr. Bo Wang completed his Ph.D. in Computer Application Technology from Harbin Engineering University in 2012, where he developed a strong foundation in advanced computational methods. Prior to this, he earned an M.A. in Computer Software and Theory from Harbin University of Science and Technology in 2007, focusing on software architecture and theoretical frameworks. His academic journey is marked by a commitment to interdisciplinary learning, enabling him to bridge gaps between theory and practical applications. His education has been pivotal in driving innovations in artificial intelligence and electronic information systems, laying the groundwork for his impactful career. 📘

Experience

Dr. Wang is currently an Associate Professor at Guangdong Polytechnic of Science and Technology, where he leads research in artificial intelligence applications. From 2014 to 2021, he served as an Associate Professor in the Department of Electronic Information Science and Technology at Harbin University of Science and Technology, contributing to groundbreaking research in electronic systems. Additionally, he was a Postdoctoral Fellow at the Key Laboratory of Molecular Imaging at the Institute of Automation, Chinese Academy of Sciences, from 2013 to 2016. His experience reflects a deep engagement with cutting-edge technologies and interdisciplinary research. 🌐

Awards and Honors

🏆 Dr. Wang has received numerous accolades for his contributions to research and academia. His achievements include recognition through prestigious grants such as the Young Natural Science Foundation of Heilongjiang Province and the University Nursing Program for Young Scholars with Creative Talents. These awards highlight his innovative approaches to artificial intelligence and electronic information systems. His dedication to advancing technology and fostering academic excellence has earned him a reputation as a leader in his field. 🎖️

Research Focus

🔍 Dr. Wang’s research centers on artificial intelligence, electronic information systems, and molecular imaging. His projects include developing new-generation electronic information technologies and advancing intelligent systems. Funded by prominent organizations, his work addresses key challenges in AI applications and explores innovative solutions in molecular imaging. His interdisciplinary approach bridges computational theories with practical applications, contributing to significant advancements in science and technology. His research has far-reaching implications for healthcare, communication systems, and intelligent automation. 🌟

Publication Top Notes:

  • Hierarchical Deep Learning Networks for Classification of Ultrasonic Thyroid Nodules
    • Year: 2022
    • Citations: 4
  • Ultrasound Image Segmentation Method of Thyroid Nodules Based on the Improved U-Net Network
    • Year: 2022
    • Citations: 5
  • Super-Resolution Swin Transformer and Attention Network for Medical CT Imaging
    • Year: 2022
    • Citations: 4
  • A Dense Visual SLAM Method in Dynamic Scenes
    • Year: 2023
    • Citations: 2
  • Segmentation Algorithm of Breast Tumor in Dynamic Contrast-Enhanced Magnetic Resonance Imaging Based on Network with Multi-scale Residuals and Dual-domain Attention
    • Year: 2023
    • Citations: 2

 

Assoc Prof Dr. Khaled EL Sayed | AI Awards | Best Researcher Award-3044

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Assoc Prof Dr. Khaled EL Sayed | AI in medicine | Best Researcher Award

Assoc Prof Dr. Khaled EL Sayed, Benha University, Egypt

Prof. Dr. Khaled El Sayed is an esteemed Associate Professor of Biomedical Engineering at Benha University, Egypt, with a comprehensive academic background including a B.Sc., M.Sc., and Ph.D. from Cairo University, specializing in hand geometry verification, protein function prediction, and EEG dynamics. He holds a Diploma in Medical Radiation Protection and is pursuing DBA studies. His notable achievements include awards for Excellence in Graduate Studies and patents for innovative medical systems, including a smart treatment system for heat/sun stroke and a smart patient mattress disinfection system. Prof. El Sayed has extensive teaching experience and has held significant roles, such as heading the Biomedical Department at MTI and consulting for various organizations. Currently, he is also a Medical Planning Consultant for ECG, Executive Manager at the Medical Equipment Manufacture Incubator (MED-Tech), and oversees the Medical Equipment Calibration Lab at Benha University. His expertise extends to BCI, electronic and microcontroller design, and infection control, and he contributes as a reviewer and board member for prominent journals in his field.

Professional Profile🌍

Orcid

Suitability for the Best Researcher Award

Prof. Dr. Khaled El Sayed is highly suitable for the Best Researcher Award due to the following reasons:

  1. Extensive Experience and Expertise: His broad experience spans academia, industry, and consultancy, showcasing his comprehensive understanding and leadership in biomedical engineering. His roles in teaching, research, and management highlight his multifaceted expertise.
  2. Significant Contributions: Prof. El Sayed’s work in developing innovative medical systems and his patents demonstrate a significant impact on medical technology. His contributions in bioinformatics and medical planning underscore his research excellence.
  3. Academic and Research Achievements: His extensive teaching experience, research publications, and editorial roles reflect his commitment to advancing knowledge in his field. His involvement in high-impact journals and conferences further illustrates his active participation in the research community.
  4. Leadership and Management: His leadership roles in various projects, including managing medical equipment incubators and calibration labs, demonstrate his capability in steering important initiatives and fostering collaboration.
  5. Awards and Recognition: His recognition through awards and patents, coupled with his ongoing DBA thesis, highlights his continued dedication to research and development.

Educational Background:

Prof. El Sayed earned his B.Sc., M.Sc., and Ph.D. in Biomedical Engineering from Cairo University, with notable research on hand geometry verification, protein function prediction, and EEG dynamics. His academic journey includes a Diploma in Medical Radiation Protection and ongoing DBA studies. 🎓

Prizes and Patents:

He has been awarded for Excellence in Graduate Studies and holds patents for a smart system for treating heat/sun stroke and a smart patient mattress disinfection system. 🏅

Professional Experience:

He has extensive experience teaching Biomedical Engineering courses at Benha University, previously headed the Biomedical Department at MTI, and consulted for various organizations. His earlier roles include Senior Biomedical Engineer at Dar Al-Fouad Hospital and Electronics Instructor at Cairo University. 📚

Current Positions:

Prof. Dr. Khaled El Sayed is an Associate Professor at Benha University in Egypt, specializing in Biomedical Engineering. He also serves as a Medical Planning Consultant for ECG, Executive Manager at the Medical Equipment Manufacture Incubator (MED-Tech), and Executive Manager of the Medical Equipment Calibration Lab at Benha University. Additionally, he is a Biomedical Engineering Consultant for the Egyptian Engineering Syndicate and a Board Member of the Egyptian Biomedical Engineering Society. 🏥

Special Skills and Interests:

Prof. El Sayed is proficient in PC software, MATLAB, and various programming languages. His interests include BCI, electronic design, microcontroller design, and infection control. He is fluent in Arabic and English. 💻

Editorial and Review Positions:

He contributes as a reviewer and editorial board member for journals such as AJMB and the American Journal of Bioinformatics Research. 📝

Publication Top Notes:

  • Title: A Low-Cost and PC-Based Automatic Hand Geometry Verification System
    • Year: 2009
  • Title: Comparison Between Different Methods for Protein Function Prediction
    • Year: 2009
  • Title: Estimation of the Correlation Between Protein Sub-Function Categories Based on Overlapping Proteins
    • Year: 2010
  • Title: Exploring Protein Functions Correlation Based On Overlapping Proteins and Cluster Interactions
    • Year: 2011
  • Title: Determining the Relations Between Protein Sub-Function Categories Based On Overlapping Proteins
    • Year: 2011

 

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. Xiaoyin’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.

🌐 Professional Profile:
Google Scholar

🎓 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.

🔬 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.

💼 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.

🔬 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.

📚 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.

🏆 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

 

 

 

Dr. Kuanxin Shen | Artificial intelligent Awards | Best Researcher Award

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Dr. Kuanxin Shen | Artificial intelligent Awards | Best Researcher Award

Dr. Kuanxin Shen , Shenyang University of Technology , China

Kuanxin Shen, a 27-year-old PhD candidate in Control Science and Engineering at Shenyang University of Technology, China, focuses his research on 3D Gaze Estimation using neural networks and deep learning. He holds a Bachelor’s degree earned in 2021 and a Master’s degree obtained in 2024. From 2022 to 2024, Kuanxin worked as an Algorithm Developer at Ningbo Chunjian Electronic Technology Co., Ltd., specializing in 3D intelligent perception in automotive cockpits. His research achievements include a Chinese utility model patent, several Chinese computer software copyrights, and three Chinese invention patents related to 3D eye tracking and gaze estimation. Kuanxin has also authored a book on industrial robot simulation and published a paper in the journal Sensors. He has been recognized with the third prize in the Henan Province College Students’ Robot Innovation Competition, a first-class academic scholarship, and the Excellent Thesis Award from Liaoning Province for his master’s thesis.

Professional Profile:

Orcid

🎓Educational Background:

Kuanxin Shen earned his Bachelor’s degree in 2021 and obtained his Master’s degree in 2024. He is currently pursuing a PhD in Control Science and Engineering at Shenyang University of Technology, China.

🏢Work Experience:

From 2022 to 2024, Kuanxin Shen worked as an Algorithm Developer at Ningbo Chunjian Electronic Technology Co., Ltd., specializing in 3D intelligent perception in automotive cockpits. His responsibilities included driver gaze detection, skeleton landmark detection, behavior detection (such as smoking and phone usage), and the detection of driver distraction and fatigue. His tasks encompassed image processing, 3D detection, data annotation, cleaning, augmentation, neural network training, and model testing.

🏆Awards and Recognitions:

During his undergraduate studies, Kuanxin Shen won the Third Prize in the 6th Henan Province College Students’ Robot Innovation Competition. In April 2024, he was awarded the First-Class Academic Scholarship during his master’s studies. In May 2024, his master’s thesis titled “Research on the Application of Driver Gaze Estimation Technology in DMS” received the Excellent Thesis Award from Liaoning Province, China

Publication Top Notes:

Title: Model-Based 3D Gaze Estimation Using a TOF Camera

  • Journal: Sensors