Mr. Fangzhou Lin | Deep Learning | Best Scholar Award

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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

 

Assoc Prof Dr. Wenlong Hang | Artificial Intelligence Award | Best Researcher Award

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Assoc Prof Dr. Wenlong Hang | Artificial Intelligence Award | Best Researcher Award

Assoc Prof Dr. Wenlong Hang, Nanjing Tech University, China

Wenlong Hang holds a Doctor of Engineering degree from Jiangnan University, where he graduated in June 2017, specializing in Light Industry Information Technology. During his doctoral studies, he visited both Hong Kong Polytechnic University and the Shenzhen Institutes of Advanced Technology. Since September 2017, Dr. Hang has been a faculty member at the School of Computer Science and Technology at Nanjing Tech University. His research interests primarily focus on artificial intelligence and machine learning, with a particular emphasis on medical image analysis and EEG signal processing. He has published more than 30 papers in reputable journals and conferences, contributing significantly to semi-supervised learning, federated learning, and EEG classification techniques. His representative works include research on medical image segmentation, reliability-aware semi-supervised frameworks, and domain-generalized EEG classification.

Professional Profile:

Summary of Suitability for Best Researcher Award :

Wenlong Hang is highly suitable for the Best Researcher Award based on his extensive research and contributions in the fields of artificial intelligence, machine learning, and medical image processing. His academic background, with a Doctor of Engineering degree from Jiangnan University, and professional experiences at institutions like Hong Kong Polytechnic University and Shenzhen Institutes of Advanced Technology, demonstrates his deep involvement in advanced technological research.

Education:

  • Doctor of Engineering (Graduated in June 2017)
    • Major: Light Industry Information Technology
    • Institution: Jiangnan University
    • Doctoral Visits: Hong Kong Polytechnic University, Shenzhen Institutes of Advanced Technology

Work Experience:

  • Since September 2017: Faculty Member
    • Position: Professor at the School of Computer Science and Technology
    • Institution: Nanjing Tech University

Research Areas:

  • Artificial Intelligence (AI)
  • Machine Learning (ML)
  • Medical Image Segmentation
  • EEG Classification

Publication top Notes:

CITED: 109
CITED: 109
CITED: 73
CITED: 67
CITED: 34
CITED: 33

 

Assoc Prof Dr. Izabela Rojek | Artificial Intelligence | Best Researcher Award

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Assoc Prof Dr. Izabela Rojek | Artificial Intelligence | Best Researcher Award 

Assoc Prof Dr. Izabela Rojek, Kazimierz Wielki University, Poland

Dr. Izabela Rojek is a prominent academic and researcher serving as the Head of the Department of Data Processing Methods and Tools and the Dean of the Faculty of Computer Science at Kazimierz Wielki University in Bydgoszcz, Poland. She holds the qualifications of Ph.D., D.Sc.Eng., and Associate Professor. Dr. Rojek’s research is centered on engineering sciences, specifically in Technical Informatics, Telecommunications, and Mechanical Engineering. Her extensive scientific output includes five books, 190 articles and chapters in monographs, and over 6000 points in the Ministry of Science and Higher Education (MNiSW) ranking, with a Hirsch index of 18 (Web of Science and Scopus) and 20 (Google Scholar). She has been recognized with 15 national and international awards, including four UKW Rector’s Awards and three foreign medals for outstanding inventions. Dr. Rojek’s contributions extend to 20 grants and innovation projects, and she actively participates in the Manufacturing Engineering Committee of the Polish Academy of Sciences, where she chairs the Manufacturing Digitisation Section.

Professional Profile:

 

Suitability for Best Researcher Award:

Izabela Rojek is an exemplary candidate for the Best Researcher Award due to her outstanding contributions to the field of engineering sciences, particularly in Technical Informatics and Telecommunications. Her extensive publication record, high citation metrics, and significant involvement in national and international research projects highlight her impact on the field. Her leadership roles and innovative research further demonstrate her exceptional qualifications for this award.

Education:

  • Ph.D. in Engineering Sciences from Kazimierz Wielki University
  • D.Sc.Eng. (Doctor of Science in Engineering)
  • Associate Professor (Assoc. Prof.)

Work Experience:

  • Kazimierz Wielki University, Bydgoszcz
    • Head of the Department of Data Processing Methods and Tools
    • Dean of the Faculty of Computer Science

Additional Roles and Experience:

  • Member of the Manufacturing Engineering Committee of the Polish Academy of Sciences
  • Chair of the Manufacturing Digitisation Section of this Committee
  • Participation in the implementation of the IFS Applications IT system, including solution design, data migration, and training material preparation

Research and Contributions:

  • Authored 5 books and over 190 articles and chapters in monographs
  • Achieved over 6000 points in MNiSW (Polish Ministry of Science and Higher Education) evaluation
  • Total Impact Factor (IF) above 120
  • Hirsch index: h=18 (573 citations, Web of Science), h=18 (717 citations, Scopus), h=20 (1097 citations, Google Scholar)
  • Involved in 20 grants and innovation projects and 10 research topics
  • Recipient of 15 national and international awards, including 4 UKW Rector’s Awards and 3 foreign medals for outstanding inventions

Publication top Notes:

 

Enhancing 3D Printing with Procedural Generation and STL Formatting Using Python

Green Energy Management in Manufacturing Based on Demand Prediction by Artificial Intelligence—A Review

Use of Machine Learning to Improve Additive Manufacturing Processes

Review of the 6G-Based Supply Chain Management within Industry 4.0/5.0 Paradigm

Utilizing Selected Machine Learning Methods for Conicity Prediction in the Process of Producing Radial Tires for Passenger Cars