Jongseo Lee | Smart Building Management via AI solutions | Best Researcher Award

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Mr. Jongseo Lee | Smart Building Management via AI solutions | Best Researcher Award

Robotics Engineer,Samsung E&A (Samsung Engineering), South Korea

Jonseo Lee is an AI-driven robotics specialist with over 11 years of diverse engineering experience in industrial automation and construction. Currently serving as a Robotics Engineer at Samsung E&A, he focuses on smart automation, AI-powered welding systems, and computer vision integration for robotics in construction. Jonseo holds a Professional Engineer (PE) license in Thermal and Fluid Systems and has worked across various sectors, including semiconductor projects, HVAC system design, and large-scale power plant construction. His expertise spans engineering design, project management, and technical leadership. Jonseo’s innovative work includes developing a 5G welding system using industrial robots, AI applications for reinforcement learning, and creating digital twin environments for construction. His academic background includes a Master’s degree in Big Data AI from Seoul National University. With a proven track record of delivering high-efficiency solutions, Jonseo is dedicated to advancing industrial automation and construction technology.

Profile

Scopus

Strengths for the Award

  1. Deep Technical Expertise in AI and Robotics for Industrial Applications:
    • Jonseo Lee’s experience in AI-driven robotics, particularly in automation for construction and industrial applications, demonstrates a strong interdisciplinary approach. His work on AI-powered adaptive welding systems, computer vision integration in robotics, and reinforcement learning showcases a solid foundation in both AI and mechanical engineering. These innovations are directly relevant to advancing automation in construction, manufacturing, and related fields.
    • His development of cutting-edge solutions, such as the automated pipe 5G welding system and digital twin environments for construction, aligns with the future of smart construction and industrial automation.
  2. Leadership and Engineering Management:
    • With a professional background that spans project management, team leadership, and cross-disciplinary collaboration, Jonseo has consistently demonstrated strong leadership and problem-solving capabilities. His roles as an Engineering Manager and Robotics Engineer at Samsung E&A reflect his ability to manage complex projects and integrate various engineering disciplines. This strategic oversight is crucial for advancing interdisciplinary research and ensuring successful project execution.
  3. Innovative Contributions to Big Data and AI in Industrial Engineering:
    • The establishment of a Big Data platform for AI applications and the use of reinforcement learning for welding robots is indicative of Jonseo’s ability to combine advanced computational techniques with practical applications in industrial settings. His academic contributions, such as the research paper on “Forecasting Building Operation Dynamics Using a Physics-Informed Spatio-Temporal Graph Neural Network,” highlight his capability to bridge theory with real-world applications. This is particularly important in fields like energy, manufacturing, and construction, where data-driven decision-making is increasingly valuable.
  4. Cross-Cultural and International Experience:
    • Jonseo’s extensive international experience working across different continents (South Korea, Kazakhstan, Saudi Arabia, etc.) with diverse teams showcases his ability to navigate complex cultural and business landscapes. His experience in managing multinational projects, such as the Balkash Thermal Power Plant and Yanbu Thermal Power Plant, is a significant strength in a globalized research environment.
  5. Proven Publication Record:
    • Jonseo has published multiple conference papers related to display technologies and optical performance, which underscores his ability to conduct meaningful and high-quality research. While his publications focus on display technology, they also highlight his ability to engage with cutting-edge research and contribute to interdisciplinary fields like human perception and visual performance. His growing body of work in AI-driven robotics and industrial engineering will further solidify his research reputation.

Areas for Improvement

  1. Broader Publication Impact and Visibility in AI and Robotics:
    • While Jonseo has demonstrated technical prowess in AI and robotics, expanding his publication portfolio in high-impact journals and conferences related specifically to AI, robotics, and industrial automation could enhance his visibility in these fields. Given his background in both engineering and AI, focusing on journal papers and larger-scale collaborations would provide further opportunities to shape the research discourse in these areas.
  2. Expansion of Research into New Technological Domains:
    • Jonseo’s focus has been predominantly on automation in construction, thermal power, and welding. Expanding his research into emerging fields like AI for sustainable construction, autonomous machinery, or energy-efficient robotics could bring additional recognition and help position him as a leader in these rapidly evolving fields.
  3. Public Engagement and Collaboration with Academia:
    • Engaging more actively with academic institutions, either through guest lectures, collaborative research, or teaching, could help Jonseo expand his influence and contribute to mentoring the next generation of engineers and researchers. Collaborating with more academic researchers in the AI field, especially in theoretical and applied aspects, could also help bridge the gap between academia and industry.

Education

Jonseo Lee completed his Bachelor of Science in Aerospace Engineering from Purdue University (2013), which laid the foundation for his engineering expertise. Building on this, he pursued a Master’s degree in Big Data AI in Industrial Engineering from Seoul National University (2024). His academic work combines advanced data analytics, artificial intelligence, and industrial systems, focusing on how AI can transform engineering applications. His master’s research led to the development of a Physics-Informed Spatio-Temporal Graph Neural Network (PISTGNN) for forecasting building operation dynamics, contributing to the integration of AI in smart construction and operations. The use of machine learning and big data analytics in engineering applications is a key area of Jonseo’s academic and professional focus. His strong technical foundation in aerospace engineering, combined with deep expertise in AI and industrial engineering, positions him as a leading figure in both practical and theoretical research.

Experience 

Jonseo Lee’s career spans over a decade in engineering roles, specializing in robotics, industrial automation, and construction. As a Robotics Engineer at Samsung E&A (2024-Present), he leads initiatives in AI-driven smart construction and automated welding systems using industrial robots. His work includes setting up big data platforms for AI applications and integrating reinforcement learning into welding robots. Jonseo previously worked as an Engineering Manager at Samsung Engineering (2020-2022), where he oversaw semiconductor projects, managed client communications, and coordinated multiple engineering disciplines. His earlier roles include designing cleanroom HVAC systems for biopharmaceutical factories and working as a boiler engineer for large-scale power plants. He also served as a piping supervisor on major thermal power projects in the Middle East. Jonseo’s diverse experience in industrial systems, AI, and construction makes him a key player in modernizing engineering practices.

Research Focus 

Jonseo Lee’s research focus lies at the intersection of artificial intelligence (AI), industrial automation, and construction technologies. He is particularly interested in the development of AI-powered adaptive systems for manufacturing and construction applications. His work includes developing reinforcement learning algorithms to enable self-adaptive robotic welding systems and applying computer vision to improve robotic precision in welding. A major part of his research also involves the integration of big data platforms for AI applications in industrial settings, allowing real-time analysis and optimization. His academic research, particularly his work on Physics-Informed Spatio-Temporal Graph Neural Networks (PISTGNN), focuses on smart building operations and forecasting dynamic performance of buildings. Jonseo is passionate about the potential of AI and robotics to revolutionize the construction industry, making it more efficient, sustainable, and adaptable to modern challenges. His contributions aim to create smarter, more autonomous systems in construction and manufacturing.

Publications

  • Measurement method for image sticking using CSF (Contrast Sensitivity Function) 📊
  • Introduction of transparent LCD displays 💡
  • Perceptual viewing-angle performance measurement method of displays 📏
  • Gray to gray crosstalk analysis considering human perception in 3D displays 🖥️
  • Optical performance analysis method of auto-stereoscopic 3D displays 🔍
  • Advanced display motion induced color distortion and crosstalk analysis methods 🎨
  • Novel technology for view angle performance measurement 🔄
  • Advanced motion induced color artifact analysis methods in FPD 📱
  • Advanced motion induced color artifact analysis methods in FPD (2nd publication) 🖥️

Conclusion

Jonseo Lee is a highly qualified candidate for the Best Researcher Award due to his innovative contributions to AI-driven robotics and industrial automation, his leadership experience in high-profile projects, and his ability to merge theory with practice in complex engineering environments. His work not only has substantial impact within the construction and industrial sectors but also has the potential for broader applications in other fields like energy and smart cities. To further solidify his standing as a leading researcher, expanding his publication record in higher-impact academic journals and exploring new research avenues would be beneficial.Given his technical skills, leadership in managing diverse projects, and his drive to incorporate AI in practical applications, Jonseo Lee is highly deserving of this recognition.