Mr. Fangzhou Lin | Deep Learning | Best Scholar Award

Posted on by ScienceFather

Mr. Fangzhou Lin | Deep Learning | Best Scholar Award 

Mr. Fangzhou Lin, Hong Kong University of Science and Technology, Hong Kong

Fangzhou Lin is a Ph.D. researcher in Civil Engineering at the Hong Kong University of Science and Technology (HKUST), specializing in deep learning, machine vision, construction robots, and multimodal data fusion. He holds a Bachelor’s degree in Civil Engineering from Fuzhou University (2015-2019) and a Master’s degree in Structural Engineering from Southeast University (2019-2022). Fangzhou Lin’s research focuses on the integration of artificial intelligence and robotics in construction automation, with applications in fire safety inspection, resource management, visual measurement, and quality assessment. His work has been published in leading journals such as Automation in Construction, Computer-Aided Civil and Infrastructure Engineering, and Advanced Engineering Informatics. He has contributed to multiple cutting-edge studies on robotic systems for construction site management, vision-based measurement techniques, and reinforcement learning-based scheduling for electric concrete vehicles. As an emerging scholar in construction automation and AI-driven inspection technologies, Fangzhou Lin actively collaborates on multi-disciplinary research projects to enhance efficiency, safety, and sustainability in the built environment. His contributions to automated reality capture, rebar positioning, and construction robotics are shaping the future of intelligent construction and infrastructure development.

Professional Profile:

SCOPUS

Suitability of Fangzhou Lin for the Best Scholar Award

Fangzhou Lin is an outstanding early-career scholar with a strong background in deep learning, machine vision, construction robotics, and multimodal data fusion within the field of civil engineering. His academic trajectory, research productivity, and innovative contributions make him a compelling candidate for the Best Scholar Award. Below is a detailed assessment of his suitability based on key criteria.

🎓 Education

  • 2015.09 – 2019.06 | Fuzhou University – Bachelor’s Degree in Civil Engineering
  • 2019.09 – 2022.06 | Southeast University – Master’s Degree in Structural Engineering
  • 2022.09 – Present | Hong Kong University of Science and Technology – Ph.D. in Civil Engineering

🏗️ Work & Research Experience

  • Expertise in: Deep learning, machine vision, construction robots, multimodal data fusion
  • Published in top journals such as Automation in Construction and Computer-Aided Civil and Infrastructure Engineering
  • Conducting research on:
    • 🔥 Fire Safety Inspection using AI-driven visual inspection
    • 🤖 Robotics for Construction Management with multi-task planning and automatic grasping
    • 🏗️ BIM-integrated Reality Capture for indoor inspection using multi-sensor quadruped robots
    • 🎯 Vision-based Monitoring for assembly alignment of precast concrete bridge members

🏆 Achievements & Awards

  • Published multiple high-impact journal papers 📚
  • Lead researcher on innovative construction technology projects 🔍
  • Contributed to advanced AI-driven automation for civil engineering 🤖
  • Research works under review in prestigious engineering journals 🏅
  • Collaborated with leading experts in civil engineering and robotics 🤝

Publication Top Notes:

Efficient visual inspection of fire safety equipment in buildings

 

Dr. Peng Zhi | Deep Learning | Best Researcher Award

Posted on by ScienceFather

Dr. Peng Zhi | Deep Learning | Best Researcher Award 

Dr. Peng Zhi, Lanzhou University, China

Peng Zhi is a Ph.D. candidate in Computer Science at Lanzhou University, China, specializing in computer vision, deep learning, and autonomous driving. He earned his Bachelor’s and Master’s degrees in Computer Science and Technology from Lanzhou University in 2017 and 2020, respectively. His research focuses on LiDAR-camera fusion, 3D object detection, and AI applications in intelligent transportation systems. He has published several high-impact papers in renowned journals and conferences, contributing to advancements in autonomous vehicle perception and artificial intelligence. Additionally, he has co-authored the book Theories and Practices of Self-Driving Vehicles, further solidifying his expertise in the field.

Professional Profile:

ORCID

Summary of Suitability for the Best Researcher Award

Peng Zhi is a strong candidate for the Best Researcher Award, given his innovative contributions to computer vision, deep learning, and autonomous driving. As a Ph.D. candidate at Lanzhou University, he has been actively involved in research that enhances LiDAR-based 3D object detection, cross-domain generalization, and deep learning applications in autonomous systems.

🎓 Education

  • Ph.D. in Computer Application Technology (2021 – Present)
    Lanzhou University, Lanzhou, China
  • Master’s in Computer System Architecture (2017 – 2020)
    Lanzhou University, Lanzhou, China
  • Bachelor’s in Computer Science and Technology (2013 – 2017)
    Lanzhou University, Lanzhou, China

💼 Work Experience

  • Ph.D. Candidate & Researcher (2021 – Present)
    Lanzhou University, Lanzhou, China

    • Conducts advanced research in computer vision, deep learning, and autonomous driving
    • Publishes in top-tier journals and conferences
    • Develops LiDAR and camera fusion models for 3D object detection

🏆 Achievements & Contributions

  • Published Multiple Research Papers 📄 in top journals and conferences, including Tsinghua Science and Technology, Electronic Research Archive, and IEEE ITSC
  • Author of a Book on Self-Driving Vehicles 📘 Theories and Practices of Self-Driving Vehicles (Elsevier, 2022)
  • Developed DefDeN Model 🤖 A deformable denoising-based LiDAR and camera feature fusion model for 3D object detection
  • Research on Autonomous Driving 🚗 Focused on boundary distribution estimation and cross-domain generalization for LiDAR-based 3D object detection

🏅 Awards & Honors

  • Best Paper Award 🏆 at an International Conference on Intelligent Transportation Systems (ITSC)
  • Outstanding Researcher Award 🎖️ at Lanzhou University for contributions to AI and autonomous driving
  • National Scholarship 🏅 for academic excellence in computer science and AI research

Publication Top Notes:

Cross-Domain Generalization for LiDAR-Based 3D Object Detection in Infrastructure and Vehicle Environments

Aurélie Cools | Deep Neural Networks | Best Researcher Award

Posted on by ScienceFather

Aurélie Cools | Deep Neural Networks | Best Researcher Award

Ms. Aurélie Cools, University of Mons, Belgium.

Aurélie Cools is a Ph.D. candidate in Engineering Sciences at the University of Mons (UMons), specializing in deep neural networks and dimensionality reduction for CBIR search engines. She holds dual Master’s degrees: Civil Engineering in Computer Science and Management (Summa Cum Laude) and Management Engineering (Magna Cum Laude), showcasing her expertise in software engineering, business analytics, and optimization. Alongside her research, she contributes as a teaching assistant at UMons. With a strong foundation in Python, SQL, and PyTorch, Aurélie is multilingual and adept at problem-solving, team management, and communication. 🌟👩‍💻📚

Publication Profile

Orcid

Education and Experience

Education 📘

  • Ph.D. in Engineering Sciences
    • Institution: University of Mons (UMons), Polytechnic Faculty
    • Thesis Topic: CBIR search engine with deep neural networks and dimensionality reduction methods
    • Duration: 2021 – Present
  • Master’s in Civil Engineering (Summa Cum Laude)
    • Institution: UMons, Polytechnic Faculty
    • Specialization: Software Engineering and Business Intelligence
    • Duration: 2018 – 2021
  • Master’s in Management Engineering (Magna Cum Laude)
    • Institution: UCL Mons
    • Specialization: Business Analytics – Logistics and Transportation
    • Duration: 2015 – 2017
  • Bachelor’s in Management Engineering (Cum Laude)
    • Institution: UCL Mons
    • Duration: 2012 – 2015

Experience 💼

  • Teaching Assistant & Ph.D. Student
    • Institution: UMons
    • Duration: September 2021 – Present
  • Credit Analyst
    • Institution: CPH Bank, La Louvière
    • Duration: July 2017 – August 2021
  • Student Worker
    • Institution: Colruyt Group, Mons
    • Duration: March 2013 – December 2016

Suitability For The Award

Ms. Aurélie Cools is an outstanding candidate for the Best Researcher Award, combining academic excellence with impactful research. Currently pursuing a Ph.D. in Engineering Sciences at the University of Mons, her work on CBIR systems using deep neural networks and dimensionality reduction demonstrates innovation and technical expertise. With dual Master’s degrees in Civil and Management Engineering earned with high honors, Aurélie excels in both research and practical applications. Her proficiency in programming, data analysis, and problem-solving, coupled with strong communication skills, makes her a deserving nominee.

Professional Development

Aurélie excels in the realms of engineering and management, leveraging cutting-edge techniques like deep neural networks and dimensionality reduction. 📊💡 Her research bridges technical and analytical fields, emphasizing CBIR technologies for efficient image retrieval. With years of experience as a teaching assistant, she fosters innovation and critical thinking among students. Aurélie’s blend of programming skills in Python, SQL, and PyTorch, coupled with proficiency in tools like MongoDB and Excel, enhances her adaptability in diverse challenges. A polyglot and skilled communicator, she thrives in team management, problem-solving, and delivering impactful solutions. 🚀🌍✨

Research Focus

Aurélie’s research focuses on developing advanced Content-Based Image Retrieval (CBIR) systems, leveraging deep neural networks and cutting-edge dimensionality reduction techniques to enhance image search and analysis efficiency. Her interdisciplinary approach combines software engineering, artificial intelligence, and data science for innovative solutions. 🖼️🤖📊 With a keen interest in the practical applications of CBIR, such as medical imaging or multimedia management, Aurélie contributes to expanding the potential of machine learning in real-world scenarios. Her expertise lies at the intersection of engineering precision and computational intelligence, making her a significant contributor to AI-driven image processing. 🌟🔍📈

Publication Top Notes

  • A New Comparative Study of Dimensionality Reduction Methods in Large-Scale Image Retrieval (2022) 📚 | Published: 2022-05-13
  • A Comparative Study of Reduction Methods Applied on a Convolutional Neural Network (2022) 📖 | 🗓️ Published: 2022-04-28

Mr. Zhongwen Hao | Deep learning Award | Best Researcher Award

Posted on by ScienceFather

Mr. Zhongwen Hao | Deep learning Award | Best Researcher Award 

Mr. Zhongwen Hao, Cranfield University, China

Zhongwen Hao is a Master’s candidate in Aerospace Manufacturing at Cranfield University, UK, and concurrently pursuing a Master of Mechanical Engineering at Nanjing University of Aeronautics and Astronautics, China. He completed his Bachelor’s degree in Electronic Information with a focus on Image Processing from China University of Mining and Technology. His research interests include robot control, visual servoing, image processing, and deep learning. Zhongwen has led notable projects such as visual servoing of robotic arms using deep learning techniques and galaxy image classification. His proficiency in programming with C++, Python, and MATLAB, coupled with his skills in deep learning and image processing, underscores his technical expertise. He has published research on motion prediction and object detection in visual servoing systems. Zhongwen is known for his strong project execution abilities, team spirit, and resilience.

Professional Profile:

Summary of Suitability:

Hao’s research direction aligns well with cutting-edge fields such as robot control, visual servoing, image processing, and deep learning. These areas are highly relevant and significant in contemporary technological advancements. Hao has a solid educational foundation with advanced studies in Aerospace Manufacturing and Mechanical Engineering, complemented by a bachelor’s degree in Electronic Information with a focus on Image Processing. This diverse yet interconnected educational background enhances his research capabilities.

Education

  1. Cranfield University, Bedford, UK
    Master’s Candidate of Aerospace Manufacturing
    Major: Deep Learning and Image Processing
    September 2023 – September 2024
  2. Nanjing University of Aeronautics and Astronautics, Nanjing, China
    Master of Mechanical Engineering
    Major: Mechanical
    September 2022 – June 2025 (Expected)
  3. China University of Mining and Technology, Xuzhou, China
    Bachelor of Electronic Information
    Major: Image Processing
    September 2017 – June 2021

Work Experience

  1. Project Leader
    Research on Visual Servoing of Robotic Arms Based on Deep Learning
    June 2024 – September 2024

    • Led research on target detection using the DETR model, trajectory planning with the PSO algorithm, and motion prediction using BiLSTM and KAN neural networks.
    • Integrated and simulated algorithms in ROS using Gazebo to validate their effectiveness.
  2. Participator
    Galaxy Image Classification Based on Deep Learning
    February 2024 – March 2024

    • Handled image preprocessing and reconstruction, and implemented galaxy image classification using the VIT model, achieving a classification accuracy of 90%.

Publication top Notes:

Motion Prediction and Object Detection for Image-Based Visual Servoing Systems Using Deep Learning