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) πŸ§ πŸ€–. With a Ph.D. in Engineering from CAS and degrees from Harbin Institute of Technology πŸŽ“, Dr. Xia specializes in industrial optical measurement, robotic vision, and intelligent manufacturing πŸ”¬πŸ“Έ. He has led innovative projects in 3D reconstruction, machine learning, and pattern recognition πŸ› οΈπŸ’‘. A key contributor to smart industry technologies, he earned recognition with the Liaoning Provincial Science and Technology Progress Award πŸ…. His work bridges advanced computer vision and real-world automation challenges .

Β Profile

πŸ”Ή Education & Experience :

Dr. Xia Renbo earned his πŸŽ“ 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 πŸŽ“ M.S. (2002) and πŸŽ“ B.S. (2000) in Mechanical Engineering and Automation from Harbin Institute of Technology. His professional journey began as an πŸ‘¨β€πŸ”¬ Assistant Researcher (2006–2008) at SIA, CAS, where he developed algorithms for photogrammetry and surface reconstruction. He then served as an πŸ‘¨β€πŸ”¬ Associate Researcher (2009–2018), focusing on 3D vision, defect detection, and camera calibration. Since 2019, he has been a leading πŸ‘¨β€πŸ”¬ Researcher at SIA, driving projects in intelligent optical measurement and robotic vision systems.

πŸ“š Professional Development :

Dr. Xia Renbo has steadily advanced his career in industrial automation and intelligent systems πŸ”§πŸ€–. Beginning as an Assistant Researcher, he contributed to early developments in 3D surface reconstruction and photogrammetry πŸ“πŸ“·. As an Associate Researcher, he expanded into multi-camera calibration and defect detection, contributing to industry-grade systems for quality assurance and control πŸ› οΈπŸ§ͺ. Now a lead Researcher, he spearheads high-impact projects in intelligent measurement and robotic vision, applying computer vision and AI to automation tasks πŸ€–πŸ”. His leadership reflects a commitment to integrating smart technologies into real-world industrial environments βš™οΈπŸŒ.

πŸ”¬ Research Focus :

Dr. Xia’s research spans several interconnected domains at the intersection of automation and intelligence πŸ§ βš™οΈ. He focuses on industrial optical measurement, advancing precision in manufacturing with 3D reconstruction and dynamic tracking technologies πŸ“πŸ”¬. His work in robotic vision and intelligent manufacturing leverages machine learning, computer vision, and pattern recognition to improve industrial adaptability and efficiency πŸ€–πŸ“Έ. By merging hardware integration with software intelligence, he contributes to the evolution of Industry 4.0 applications πŸš€πŸ­. His research enhances robotic equipment with real-time perception and adaptability, fostering smarter production lines and inspection systems πŸ› οΈπŸ“Š.

πŸ† Awards and Honors :

Dr. Xia Renbo was honored with the πŸ₯‰ 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 πŸ”πŸš†. 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 πŸ€–πŸ“

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.
β€œA 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.
β€œRobust 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.
β€œLow-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.
β€œResearch 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.
β€œA 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 researcherβ€”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. Runyi Yang | 3D and Robotics | Best Researcher Award

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Mr. Runyi Yang | 3D and Robotics | Best Researcher Award

Mr. Runyi Yang, Imperial College London, United Kingdom

Mr. Runyi Yang is a promising researcher specializing in computer vision, robotics, and artificial intelligence, currently pursuing a Ph.D. in Computer Vision and Robotics at INSAIT, Bulgaria, under the mentorship of Prof. Luc Van Gool and Dr. Danda Pani Paudel. He holds a Master of Research (MRes) in AI and Machine Learning from Imperial College London, where he focused on camera relocalization and uncertainty quantification. His research encompasses Neural Radiance Fields (NeRFs), Gaussian Splatting, 3D reconstruction, and scene understanding, with notable contributions that have led to state-of-the-art results on public datasets. Recognized with the CICAI 2023 Best Paper Runner-up Award and several accolades in AI, robotics, and mathematics competitions, Runyi is dedicated to enhancing performance and efficiency in 3D rendering and scene understanding.

Professional Profile

Google Scholar

Suitability for the Best Researcher Award:

While Mr. Yang is still at an early stage in his career, his groundbreaking research in computer vision, robotics, and AI, along with his recognitions and publications, demonstrate his potential to become a leader in these fields. His expertise in NeRFs, 3D reconstruction, and autonomous driving simulation is highly relevant to modern technological challenges, making him a strong contender for the Best Researcher Award.

Education & Expertise:

Mr. Runyi Yang is a talented researcher with a focus on computer vision, robotics, and AI. He is pursuing a PhD in Computer Vision and Robotics at INSAIT, Bulgaria, under the guidance of Prof. Luc Van Gool and Dr. Danda Pani Paudel. He holds a Master of Research (MRes) in AI and Machine Learning from Imperial College London, where he worked on camera relocalization and uncertainty quantification.

Research Focus:

Runyi’s research spans Neural Radiance Fields (NeRFs), Gaussian Splatting, 3D reconstruction, and scene understanding. He has contributed to advancing 3D implicit representation and compositional zero-shot learning, achieving state-of-the-art results on public datasets.

Achievements & Honors:

He has been recognized with the CICAI 2023 Best Paper Runner-up Award and multiple other accolades in AI, robotics, and mathematics competitions.

Current Research Interests:

His interests include camera relocalization, NeRFs, and 3D vision, with a focus on improving performance and efficiency in 3D rendering and scene understanding.

Publication Top Notes:

  • “Mars: An instance-aware, modular and realistic simulator for autonomous driving”
    • Citations: 63
    • Published: 2023
  • “GaussianGrasper: 3D Language Gaussian Splatting for Open-vocabulary Robotic Grasping”
    • Citations: 10
    • Published: 2024
  • “SUNDAE: Spectrally Pruned Gaussian Fields with Neural Compensation”
    • Citations: 4
    • Published: 2024
  • “City-scale continual neural semantic mapping with three-layer sampling and panoptic representation”
    • Citations: 4
    • Published: 2023
  • “Self-Aligning Depth-regularized Radiance Fields for Asynchronous RGB-D Sequences”
    • Citations: 2
    • Published: 2022

 

 

Dr. Josef PonikelskΓ½ | Robotics Awards | Best Researcher Award

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Dr. Josef Ponikelsky | Robotics Awards | Best Researcher Award

Dr. Josef Ponikelsky, Jan Evangelista Purkyne University in usti nad Labem, Czech Republic

Ing. Josef PonikelskΓ½,Β  is a Product Manager at HENNLICH s.r.o., where he leads a robotics team and manages technical projects. He holds a Ph.D. in Engineering Technology from the University of J. E. PurkynΔ›, ÚstΓ­ nad Labem, where he also completed his Master’s degree in Production Management. His educational background includes a focus on engineering technology and a semester of study in Greece. With prior roles in various sectors, including fitness center management and CNC machine operation, Josef has extensive experience in technical and customer-oriented positions. His achievements include multiple publications in engineering journals, participation in international conferences, and a top placement in the 2020 KONA competition. His research interests lie in collaborative robots and their collision environments.

Professional Profile:

Scopus

Suitability for Best Researcher Award

Ing. Josef PonikelskΓ½’s strong academic background, extensive research contributions, and practical experience in robotics and engineering make him an excellent candidate for the Best Researcher Award. His ongoing Ph.D. research, coupled with his significant publication record and industry achievements, underscores his impact on the field and his potential to drive further advancements in engineering technology.

πŸŽ“Educational background:

Ing. Josef PonikelskΓ½ pursued his Ph.D. in Engineering Technology at the University of J. E. PurkynΔ›, ÚstΓ­ nad Labem, from 2018 to the present. Prior to this, he completed his Master’s in Production Management at the same university, which also included a semester of study in Greece, from 2011 to 2018. He earned his diploma in StrojΓ­renstvΓ­ CAD-CAM from StΕ™ednΓ­ PrΕ―myslovΓ‘ Ε kola, Teplice, from 2007 to 2011.

🏒work experience:

Ing. Josef PonikelskΓ½ has held various positions over the years, demonstrating a diverse range of experience. Since 2016, he has been a Product Manager at HENNLICH s.r.o., where he leads a robotics team, manages technical projects, develops the customer base, and creates technical and commercial offers. In 2015, he worked as a Coordinator at Randstad, s.r.o., focusing on employee care and client satisfaction for KS Kolbenschmidt Czech Republic a.s. From 2011 to 2015, he was a Fitness Center Attendant at Sluneta Fitness, managing client relations and maintaining equipment. In 2011, he worked as a Constructor at VΓ½tahy VanΔ›rka s.r.o., designing elevators in 3D, creating drawings, and handling project documentation. Additionally, he operated CNC machines for piston machining and measurement at KS Kolbenschmidt Czech Republic a.s. in 2011, and managed package weighing and storage at PPL CZ s.r.o. from 2008 to 2011.

πŸ…Awards and achievements:

Ing. Josef PonikelskΓ½ has earned notable accolades and recognition in his field. In 2020, he achieved 1st place in the KONA JEDE NA VYSOKOU competition. He was also awarded in the Students’ Grant Competition SGS, FSI UJEP for the 2021–2022 period, where he conducted research on forces and pressures in collaborative robots. His scholarly contributions include multiple publications in prestigious engineering journals, such as StrojΓ­renskΓ‘ Technologie, Manufacturing Technology, and Sensors, as well as several conference proceedings, totaling 11 articles.

Publication Top Notes:

  • Force and Pressure Dependent Asymmetric Workspace Research of a Collaborative Robot and Human
    • Citations: 1
  • Research of Robots in Cooperative Mode in Human Body Part Detection
    • Citations: 3

 

 

 

 

Ms. Yuanjiong Ying |Β Robotics Awards |Β Best Researcher Award

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Ms. Yuanjiong Ying |Β Robotics Awards |Β Best Researcher Award

Ms. Yuanjiong Ying, Shanghai Jiao Tong University, China

Ms. Yuanjiong Ying is an accomplished M.S. student in Robotics at Shanghai Jiao Tong University, where she also earned her B.S. in Mechanical Engineering, recognized with honors such as the Outstanding Graduate Award and Academic Scholarship. With extensive internship experience at Huawei’s 2012 Lab and JAKA Robotics Co., Ltd., she has developed advanced algorithms for autonomous driving and collaborative robotics. Her project leadership includes creating state estimation algorithms for UAVs in GPS-denied environments and developing a vision-inertial SLAM system, showcasing her expertise in robotics, visual perception, and autonomous systems.

Professional Profile:

Scopus

Suitability for the Award

Ms. Yuanjiong Ying is an outstanding candidate for the Best Researcher Award due to the following reasons:

  1. Academic Excellence:
    • Ms. Ying has consistently demonstrated academic excellence throughout her studies, maintaining a top GPA and earning numerous honors and awards. Her rigorous academic background in robotics and mechanical engineering, coupled with her performance at one of China’s top universities, positions her as a leading young researcher.
  2. Research Impact:
    • Despite her relatively early career stage, Ms. Ying has already made significant contributions to the field of robotics, evidenced by her authorship of key publications and patents. Her work on multi-view active sensing, collision-evaluation systems, and efficient moving horizon estimation has the potential to advance human-robot interaction, UAV localization, and collaborative robotics.
  3. Professional Experience:
    • Her internships at Huawei and JAKA Robotics provided her with practical experience in applying her research to real-world problems, particularly in autonomous driving and collaborative robotics. Her contributions to high-resolution visual representation and safety trajectory planning demonstrate her ability to translate complex theoretical concepts into practical applications.
  4. Leadership and Innovation:
    • Ms. Ying’s leadership roles in project management and her involvement in extracurricular activities reflect her ability to lead and innovate. Her experience in organizing significant university events also suggests strong organizational and interpersonal skills, which are critical for a successful research career.

Summary of Qualifications

  1. Education:

    • M.S. in Robotics (2022.09 – 2025.06), Shanghai Jiao Tong University:
      • GPA: 3.8/4.0, Top 10%.
    • B.S. in Mechanical Engineering (2018.09 – 2022.06), Shanghai Jiao Tong University:
      • GPA: 3.7/4.3, Top 15%.
      • Honors: Outstanding Graduate Award, Outstanding Student Leader Award, Academic Scholarship, Excellence Scholarship.

  2. Publications & Patents:

    • Authored and co-authored several impactful papers and patents, including:
      • “Multi-View Active Sensing for Human-Robot Interaction via Hierarchically Connected Tree” (1st Author, SNA).
      • “CEASE: Collision-Evaluation-based Active Sense System for Collaborative Robotic Arms” (2nd Author, TIM).
      • “A Computationally Efficient Moving Horizon Estimation for Flying Robots’ Localization Regarding a Single Anchor” (2nd Author, ROBIO).
      • Patents: Proactive Safety Protection Technology for Collaborative Robots, Active Vision Algorithm Based on Cylindrical Representation of Humanoid Obstacles, Design of a Decoupled Active Vision Mechanism Enclosure.

  3. Internship Experience:

    • Huawei 2012 Lab, Central Media Technology Institute (2024.06 – Present):
      • Worked on Autonomous Driving Algorithm as an intern, focusing on high-resolution visual representation for multimodal large models in autonomous parking scenarios.
    • JAKA Robotics Co., Ltd. (2023.02 – 2024.03):
      • Developed visual perception algorithms for collaborative robots, focusing on safety trajectory planning and proactive visual perception.

  4. Project Experience:

    • Flying Robots’ Localization Algorithms Based on Multi-Sensor Data Fusion:
      • Project Lead, developed state estimation algorithms for UAV positioning in GPS-denied environments.
    • UAV Simultaneous Localization and Mapping System Based on Vision-Inertial Sensors:
      • Developed a tightly-coupled SLAM system for UAV localization.

  5. Extracurricular Activities:

    • Director of Culture and Sports Center, Shanghai Jiao Tong University Student Union (2019.03 – 2022.03):
      • Organized significant events such as the Graduation Ceremony and Anniversary Celebration.

  6. Professional Skills:

    • Programming Languages: Python, C++, MATLAB.
    • Software: PyTorch, HuggingFace, ROS, MoveIt!, Gazebo, Rviz, Linux, Git, Solidworks, AutoCAD.
    • LLMs: Proficient in LLM principles and fine-tuning; Familiar with models like LLaVA, CLIP, LLaMA, InternVL.
    • Robotic Systems: Proficient in the full operation process of robots (perception, planning, control); Familiar with VINS-Fusion framework, multi-sensor data fusion, and multi-camera environmental perception.

Conclusion

Ms. Yuanjiong Ying is a highly suitable candidate for the Best Researcher Award. Her combination of academic excellence, impactful research, professional experience, and leadership qualities makes her an exceptional contender for this recognition. Her contributions to robotics, particularly in human-robot interaction and UAV localization, highlight her potential to become a leading researcher in her field.

 

 

Mr. Viekash Vinoth Kumar | AI in Robotics | Best Researcher Award

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Viekash Vinoth Kumar | AI in Robotics | Best Researcher Award

Mr. ViekashVinoth Kumar, SambaNova Systems Inc, United States

Viekash Vinoth Kumar is a highly skilled researcher with over three years of domain experience and more than eight publications to his name. His expertise lies in AI, Computer Vision, Machine Learning, and Deep Learning. He has worked on advanced robotics and AI projects, demonstrating a strong ability to innovate and optimize complex systems. His experience spans top institutions like Carnegie Mellon University, the National University of Singapore, and the Indian Institute of Technology Madras.

Professional Profile:

Orcid

Suitability for the Research for Best Researcher Award

Viekash holds a Master of Science in Robotics from Carnegie Mellon University (CMU), where he has maintained an outstanding QPA of 4.06/4.00. He was awarded the prestigious NSF PG Scholarship, positioning him among the top 15 scholars in a highly competitive pool. He also has a Bachelor of Technology in Instrumentation and Control Engineering from the National Institute of Technology Tiruchirappalli (NITT), where he excelled academically, securing the B.Tech Institute Award for the highest GPA in his 2nd and 3rd years.

Academic Background πŸ“š

Currently, Viekash is a Graduate Student Researcher at CMU’s Biorobotics and BIG Labs. He has interned at JBT Corporation and the University of Waterloo, where he contributed to significant advancements in AI and computer vision. His work includes developing AI-driven clinical tools, enhancing medical imaging techniques, and optimizing autonomous vehicle navigation systems. His role as a Teaching Assistant and reviewer for prestigious journals further reflects his academic excellence.

Professional Experience πŸ’Ό

Currently, Viekash is a Graduate Student Researcher at CMU’s Biorobotics and BIG Labs. He has interned at JBT Corporation and the University of Waterloo, where he contributed to significant advancements in AI and computer vision. His work includes developing AI-driven clinical tools, enhancing medical imaging techniques, and optimizing autonomous vehicle navigation systems. His role as a Teaching Assistant and reviewer for prestigious journals further reflects his academic excellence.

Research Interests and Contributions πŸ”¬

Viekash’s research interests are deeply rooted in AI, specifically in Computer Vision, Machine Learning, Deep Learning, and Robotics. His projects often explore innovative solutions to complex challenges, such as developing AI architectures for medical imaging, optimizing neural networks for robotics applications, and pioneering methods for data-driven prediction and analysis. His work has resulted in several high-impact publications, reflecting his commitment to advancing the field.

Awards and Recognitions πŸ†

Viekash Vinoth Kumar has earned notable recognition for his research, including the prestigious NSF PG Scholarship at Carnegie Mellon University, awarded to top 15 scholars from over 10,000 applicants. He has also received the Best Paper Award from IEEE for his contributions to deep learning. His academic excellence was highlighted by the B.Tech Institute Award for highest GPA during his undergraduate studies. Additionally, Viekash’s work has been acknowledged through several international conference awards and prestigious journal publications, reflecting his significant impact in his field.

Innovations and Impact πŸš€

Viekash Vinoth Kumar’s innovations in AI and robotics have significantly advanced medical imaging and autonomous systems. His development of AI-driven tools for real-time medical diagnostics and enhanced imaging techniques has improved accuracy and efficiency in clinical settings. His work on autonomous vehicle navigation and optimized neural network models has advanced robotics, offering new solutions for complex challenges. These contributions not only push technological boundaries but also provide practical, impactful solutions across multiple domains.

Publication Top Notes:

  1. “Paced Curriculum Distillation with Prediction and Label Uncertainty for Image Segmentation”
    Year: 2024
  2. “CADSketchNet – An Annotated Sketch Dataset for 3D CAD Model Retrieval with Deep Neural Networks”
    Year: 2023
  3. “FAZSeg: A New Software for Quantification of the Foveal Avascular Zone”
    Year: 2022
  4. “CLEARNESS: Cross-scaLe tEmporAl gRaph NEtwork for Super-reSolutions”
    Year: 2023
  5. “Deep Learning Based Muscle Intent Classification in CPM for Knee OA Rehabilitation”
    Year: 2023

 

 

 

 

Assoc Prof Dr. Duong Vu | Robotics Awards | Best Researcher Award

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Assoc Prof Dr. Duong Vu | Robotics Awards | Best Researcher Award

Assoc Prof Dr. Duong Vu, Duy Tan University, Vietnam

Dr. Duong Vu is a renowned academic and engineer at Duy Tan University, Vietnam, with a distinguished background in Mechanical Engineering and Commerce Management. He earned his undergraduate degree from Voroshilopgrad University of Machine-Building in the Soviet Union and a Ph.D. in Plasma Spraying Technology from State Saint-Petersburg University, Russia. Dr. Vu’s research expertise spans deformation and stress analysis in welding, mechatronics, robotics, and advanced manufacturing processes, including additive manufacturing and thermal spraying technology. He has received numerous awards, including the VIFOTEC Prize and medals from Vietnam’s Ministry of Science and Technology, reflecting his significant contributions to science and technology.

Professional Profile:

Google Scholar

πŸŽ“ Education:

Dr. Duong Vu is a distinguished academic and engineer with an extensive educational background. He earned his undergraduate degree in Mechanical Engineering from Voroshilopgrad University of Machine-Building in the Soviet Union (1974-1980). Dr. Vu furthered his education with a Bachelor of Commerce Management from Hanoi University of National Economy (1995-1998) and obtained a Ph.D. in Plasma Spraying Technology from State Saint-Petersburg University, Russia (1990-1993). His diverse academic pursuits reflect a strong foundation in both engineering and management.

πŸ… Awards and Scholarships:

Dr. Duong Vu is a distinguished academic and researcher recognized for his exceptional contributions to science and technology. He earned an Outstanding Diplome and an Honorable Diplome from Voroshilopgrad University of Machine-Building for his academic excellence and research prowess. Dr. Vu has been awarded medals from the Ministry of Science and Technology and the Ministry of Industry and Commerce for his scientific and technological achievements. In 2016, he received the prestigious VIFOTEC Prize for designing an innovative automatic machine for packaging cable rolls. Additionally, Dr. Vu secured a government grant from Vietnam (2019-2020) to advance the commercialization of robots for welding quality inspection.

πŸ”¬ Research Interests:

Dr. Duong Vu is a distinguished researcher whose expertise encompasses a broad spectrum of advanced engineering disciplines. His research interests include the deformation and stress analysis in welding, the design and fabrication of antifrictional and bimetal materials, and the automatic control of production processes. He is also deeply involved in mechatronics, robotics, and the development of smart devices. Dr. Vu is at the forefront of additive manufacturing and thermal spraying technology, contributing to the advancement of materials and processes in these areas. His work in advanced manufacturing processes highlights his commitment to innovation and technological progress.

Publication Top Notes:

 

 

Mrs. Hager Elhenidy | Robotics Awards | Best Researcher Award

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Mrs. Hager Elhenidy | Robotics Awards | Best Researcher Award

Mrs. Hager Elhenidy, Benha University, Egypt

πŸŽ“ Hager Gamal is a driven Mechatronics Engineering Master’s student at Benha University, Egypt, building upon her exceptional academic background from the Higher Institute of Engineering and Technology in Mahalla Al-Kubra. Her journey is marked by excellence, highlighted by a very good graduation degree and an outstanding project on prosthetic limb development. πŸ’Ό Hager’s professional repertoire spans across freelancing, electronics maintenance engineering, and technical office roles, showcasing her adaptability and proficiency. She has contributed to diverse projects, from 3D modeling to electronics maintenance, demonstrating her versatility. πŸ’» Proficient in SOLIDWORKS, Arduino, MATLAB, and more, Hager’s software skills are matched by her creativity in Adobe Premiere and Photoshop.

Professional Profile:

Scopus

πŸŽ“ Education:

Hager Gamal is currently pursuing a Master’s degree in Mechatronics Engineering at Benha University, following her Bachelor’s degree from the Higher Institute of Engineering and Technology in Mahalla Al – Kubra. Her academic journey has been marked by excellence, with notable achievements such as a very good graduation degree and an excellent project on prosthetic limb development.

πŸ’Ό Work Experience:

With experience as a Freelancer, Electronics Maintenance Engineer, and Technical Office Engineer, Hager has demonstrated her versatility and proficiency in various roles. She has worked on diverse projects, including 3D modeling and product design, electronics maintenance, and technical office tasks.

πŸ’» Software Skills:

Proficient in SOLIDWORKS, C Language, Arduino, Art Cam, MATLAB, Proteus, and various Microsoft Office applications. Additionally, she possesses expertise in Adobe Premiere and Photoshop, along with proficiency in YouTube channel management.

🧠 Soft Skills:

Hager is a highly motivated individual with strong leadership qualities and entrepreneurship skills. She excels in problem-solving, time management, and working under pressure. Her excellent presentation and communication skills complement her technical abilities.

Publication Top Note:

1.Β  Low-cost parallel delta robot for a pick-and-place application with the support of the vision system