Mr. Fangzhou Lin | Deep Learning | Best Scholar Award

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 UniversityBachelor’s Degree in Civil Engineering
  • 2019.09 – 2022.06 | Southeast UniversityMaster’s Degree in Structural Engineering
  • 2022.09 – Present | Hong Kong University of Science and TechnologyPh.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

 

Prof. Ming-Hsiang Su | Deep Learning | Best Researcher Award

Prof. Ming-Hsiang Su | Deep Learning | Best Researcher Award 

Prof. Ming-Hsiang Su, Data Science, Soochow University, Taiwan, Taiwan

Ming-Hsiang Su is an esteemed assistant professor in the Department of Data Science at Soochow University in Taipei, Taiwan. He earned his Ph.D. in Computer Science and Information Engineering from National Chung Cheng University and has an impressive academic background with an M.S. in Management Information Systems from National Pingtung University of Science and Technology and a B.S. in Computer Science from Tunghai University. His research expertise includes spoken dialogue systems, personality trait perception, speech emotion recognition, and speech signal processing. Before his current role, Ming-Hsiang conducted postdoctoral research at National Cheng Kung University and served as a lecturer at multiple institutions, including National Pingtung University of Science and Technology and National Chung Cheng University. His professional journey also includes a stint as an R&D engineer at Cino Group. His work in deep learning, natural language processing, and emotion and personality perception has significantly contributed to advancements in speech signal processing.

Professional Profile:

ORCID

 

🎓 Education

  • Ph.D. in Computer Science and Information Engineering, National Chung Cheng University
  • M.S. in Management Information Systems, National Pingtung University of Science and Technology
  • B.S. in Computer Science, Tunghai University

💼 Work Experience

  • Assistant Professor (August 2020 – Present)
    Department of Data Science at Soochow University, Taipei, Taiwan
  • Postdoctoral Fellow (August 2013 – July 2020)
    Department of Computer Science and Information Engineering (CSIE) at National Cheng Kung University, Tainan, Taiwan
  • Lecturer (June 2013 – July 2013)
    Skill Evaluation Center of Workforce Development Agency, Ministry of Labor, Taichung City, Taiwan
  • Lecturer (February 2012 – January 2013)
    Department of Management Information Systems at National Pingtung University of Science and Technology, Pingtung, Taiwan
  • Lecturer (September 2006 – January 2013)
    Department of Mathematics at National Chung Cheng University, Chiayi, Taiwan
  • R & D Engineer (August 2003 – September 2004)
    Cino Group, Taipei, Taiwan

Ming-Hsiang Su’s career reflects his dedication to advancing the field of computer science, particularly in speech and signal processing, through a blend of academic excellence and practical research. 🌟

Publication top Notes:

Few-Shot Image Segmentation Using Generating Mask with Meta-Learning Classifier Weight Transformer Network

Implementation of Sound Direction Detection and Mixed Source Separation in Embedded Systems

Semantic-Based Public Opinion Analysis System

Conditional Adversarial Learning for Empathetic Dialogue Response Generation

Speech Emotion Recognition Considering Nonverbal Vocalization in Affective Conversations