Prof. Dr. Younggun Lee | AI in Networking Data | Best Researcher Award

Prof. Dr. Younggun Lee | AI in Networking Data | Best Researcher Award

Prof. Dr. Younggun Lee, Republic of Korea Air Force Academy, South Korea

Prof. Dr. Younggun Lee is a distinguished researcher in machine learning, video analysis, human tracking, image processing, and AI in networking data. He earned his Ph.D. in Electrical Engineering & Computer Science from the University of Washington, USA, following an M.S. from Seoul National University and a B.S. from the Republic of Korea Air Force Academy. Currently a Professor at Chungbuk National University, Dr. Lee has over a decade of academic and research experience, making groundbreaking contributions in AI-driven video analytics and network intelligence. His innovative work has earned him multiple accolades, including the Best Paper Award at IEIE and victory in the AI City Challenge at CVPR 2018. With a strong interdisciplinary background spanning engineering and military research, he continues to drive advancements in artificial intelligence applications.

Professional Profile

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🏆 Suitability for the Best Researcher Award 

Prof. Dr. Younggun Lee is an ideal candidate for the Best Researcher Award due to his groundbreaking contributions in AI-driven video analysis, human tracking, and machine learning applications. His research has significantly advanced the fields of smart surveillance, autonomous systems, and networking AI, making impactful contributions to academia and industry. Having received numerous prestigious awards, including the Best Paper Award at IEIE and the AI City Challenge Winner at CVPR 2018, his work is recognized internationally. His dedication to pushing the boundaries of image processing, AI-enhanced networking, and intelligent video systems showcases his commitment to innovation. With over a decade of research experience, Dr. Lee’s work continues to shape the future of AI in security, defense, and automation, making him a strong contender for this esteemed award.

🎓 Education 

Prof. Dr. Younggun Lee’s academic journey is marked by excellence across prestigious institutions. He earned his Ph.D. in Electrical Engineering & Computer Science from the University of Washington (2013-2017), focusing on advanced machine learning and AI-based video processing. Prior to that, he completed his M.S. from Seoul National University (2007-2009), specializing in computer vision and data-driven networking intelligence. His B.S. in Chemistry and Physics from the Republic of Korea Air Force Academy (2001-2005) provided him with a strong foundation in analytical research and defense technologies. His education, complemented by a full scholarship from the Republic of Korea Government for Ph.D. studies, reflects his academic prowess and research potential.

💼 Experience 

Prof. Dr. Younggun Lee has an extensive career in research and academia, spanning over a decade. He is currently a Professor at Chungbuk National University (since April 2023), where he leads research on AI-powered video analytics and networking intelligence. Previously, he served as an Associate Professor (2018-2023) and Assistant Professor (2012-2018) in the Department of Electronics and Communication Engineering, contributing to innovative research and mentoring young scientists. His early career included a role as an Administration Officer at the Aerospace Research Center, ROKAFA (2012-2013), where he worked on defense-related AI applications. His experience bridges academia, military research, and industry collaboration, making him a leading expert in machine learning, computer vision, and AI for security and networking applications.

🏅 Awards & Honors

Prof. Dr. Younggun Lee’s contributions to AI and networking research have been recognized with multiple prestigious awards. In 2018, he won the Best Paper Award at the IEIE Autumn Conference, highlighting his excellence in academic research. His AI-driven video analysis expertise led him to win the AI City Challenge at CVPR 2018, a global competition in computer vision and smart city applications. His academic journey was supported by a full Ph.D. scholarship from the Republic of Korea Government (2013-2016), acknowledging his research potential. In 2012, he received the Distinguished Service Medal and the ROKAFA Superintendent’s Award for his exceptional service in aerospace research. He also graduated with honors from the University of Joint Military (2012), earning the University President’s Award for high scholastic achievements. These accolades solidify his position as a leader in AI and intelligent systems research.

🔬 Research Focus

Prof. Dr. Younggun Lee’s research is at the intersection of machine learning, video analysis, human tracking, image processing, and AI in networking data. His work in AI-powered video surveillance has led to significant advancements in human activity recognition, anomaly detection, and autonomous tracking systems. His research also explores deep learning models for smart city surveillance, enhancing security and automation. Additionally, he investigates AI-driven networking technologies, optimizing real-time data transmission in complex networks. His expertise in image processing extends to applications in autonomous vehicles, military defense, and healthcare diagnostics. His groundbreaking work has been published in top-tier AI and computer vision conferences, contributing to next-generation smart surveillance, AI-assisted traffic monitoring, and intelligent security systems.

Publication Top Notes:

  • Online-learning-based human tracking across non-overlapping cameras
    • Year: 2017
    • Citations: 71
  • An ensemble of invariant features for person reidentification
    • Year: 2016
    • Citations: 35
  • Multiple-kernel based vehicle tracking using 3D deformable model and camera self-calibration
    • Year: 2017
    • Citations: 27
  • Combined estimation of camera link models for human tracking across nonoverlapping cameras
    • Year: 2015
    • Citations: 20
  • Inter-camera tracking based on fully unsupervised online learning
    • Year: 2017
    • Citations: 10

 

 

 

Prof. Reza Ghaderi | AI in Networking | Best Faculty Award

Prof. Reza Ghaderi | AI in Networking | Best Faculty Award

Prof.  Reza Ghaderil, Shahid Beheshti University, Iran

Reza Ghaderi’s extensive achievements in education, research, and academic leadership make him an exemplary candidate for the Research for Best Researcher Award. His educational background, specialized research expertise, and significant academic contributions reflect a career dedicated to advancing electrical engineering and technology. His innovative research projects further illustrate his ability to address complex challenges and drive progress in his field. With a proven track record of impactful work and leadership, Dr. Ghaderi embodies the qualities sought for this prestigious award.

Professional Profile:

Scopus 
Orcid
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Suitability for the Award

Prof. Reza Ghaderi is highly suitable for the Research for Best Faculty Award. His extensive academic and research experience, combined with his leadership roles in both educational institutions and national projects, make him a distinguished figure in the field of electrical engineering. His contributions to research, particularly in neural networks, nanoelectronics, and particle accelerators, have had a significant impact on both the academic community and the broader technological landscape.

Educational Achievements:

Reza Ghaderi’s educational background is marked by significant accomplishments, reflecting his deep knowledge and commitment to the field of electrical engineering. He earned his Bachelor’s and Master’s degrees in Electrical Engineering – Electronics from Ferdowsi University of Mashhad and Tarbiat Modares University, respectively. His academic journey culminated in a Ph.D. from the University of Surrey, Guildford, where he specialized in electrical engineering. His strong educational foundation has provided him with the skills and insights necessary for groundbreaking research and leadership in his field.

Specialized Research Expertise:

Dr. Ghaderi’s research expertise spans a wide array of topics within electrical engineering, showcasing his versatility and depth of knowledge. His work includes the design and development of advanced technological systems such as high-voltage generators and digital autopilot systems. He has made significant contributions to neural networks, face recognition systems, and hydraulic servo systems, highlighting his ability to tackle complex problems and innovate solutions in various research areas.

Significant Academic Contributions:

Reza Ghaderi has made notable academic contributions through his roles as a faculty member and administrator at prestigious institutions. His leadership positions at the University of Mazandaran and Shahid Beheshti University, including his current role as Dean of the Faculty of Electrical Engineering, underscore his influence in shaping academic programs and research initiatives. His involvement in the development of national technology and IT strategies further emphasizes his impact on advancing educational and technological standards.

Innovative Research Projects:

Dr. Ghaderi’s innovative research projects have addressed a range of technological and scientific challenges. His work on designing high-voltage generators and AC motor speed controllers, along with his research on neural networks and particle accelerators, demonstrates his ability to lead and execute complex projects. His contributions to national projects, such as the development of sonar strategies and energy strategy documents, highlight his commitment to advancing technology and its applications on a broader scale.

Professional Experience and Impact:

Reza Ghaderi’s professional experience encompasses a broad range of roles and responsibilities that highlight his profound impact on the field of electrical engineering and academia. His career trajectory demonstrates a commitment to both advancing technological innovations and shaping educational practices.

Publication Top Notes:

  • Publication Topic: “A Practical Approach to Tracking Estimation Using Object Trajectory Linearization”
    • Year: 2024
  • Publication Topic: “Rapid and Accurate Predictions of Perfect and Defective Material Properties in Atomistic Simulation Using the Power of 3D CNN-Based Trained Artificial Neural Networks”
    • Year: 2024
  • Publication Topic: “Solving a Class of Thomas–Fermi Equations: A New Solution Concept Based on Physics-Informed Machine Learning”
    • Year: 2024
  • Publication Topic: “Salinity and Flow Pattern Independent Flow Rate Measurement in a Gas-Liquid Flow with Optimum Feature Selection and Novel Detection Geometry Using ANNs”
    • Year: 2024
  • Publication Topic: “An IoT-Based Packet Aggregation Mechanism for the SDN-Based Wide Area Networks”
    • Year: 2024