Biomedical Engineering at Pukyong National University, South Korea
Nhan T. Nguyen, a Master’s student at Pukyong National University, is a promising early-career researcher specializing in biomedical engineering, computer vision, and artificial intelligence. His research focuses on non-destructive testing, low-level vision, and automated inspection systems using advanced AI techniques such as GANs, transformers, and diffusion models. Nhan has contributed to multiple peer-reviewed publications in prestigious journals like IEEE Transactions and MDPI Applied Sciences, with additional manuscripts under review and in preparation. His work demonstrates strong practical relevance, with AI models deployed in industrial applications including semiconductor inspection, robotic automation, and smart city infrastructure. He has received several academic honors and awards, reflecting his dedication and innovation. Despite being at the master’s level, he serves as a peer reviewer for international journals and conferences, highlighting his scholarly maturity. With interdisciplinary expertise, a growing publication record, and impactful real-world applications, Nhan is highly suitable for the Best Researcher Award in the early-career category.
Professional Profile
Education🎓
Nhan T. Nguyen has built a strong educational foundation in engineering and artificial intelligence across reputable institutions in Vietnam and South Korea. He earned his Bachelor of Science degree in Information Technology Engineering from Ho Chi Minh University of Technology, where he was actively involved in undergraduate research and received multiple academic awards and scholarships. During his undergraduate years, he developed projects integrating AI with OCR and chatbot systems. Currently, he is pursuing a Master’s degree in the Industry 4.0 Convergence Bionics Engineering program at Pukyong National University in South Korea under the supervision of Professor Junghwan Oh. His graduate research focuses on non-destructive testing, specifically in scanning acoustic microscopy systems, and applying AI to industrial inspection tasks. Through this academic journey, Nhan has gained in-depth knowledge and hands-on experience in computer vision, machine learning, and robotics, forming a strong educational background that supports his innovative contributions to research and industry applications.
Professional Experience📝
Nhan T. Nguyen has gained diverse professional experience in the fields of artificial intelligence, computer vision, and industrial automation. He served as an AI Engineer at the Artificial Intelligence Center of FPT Software in Vietnam, where he worked on optimizing dehumidification processes for the Chicago Art Museum and enhancing defect detection in steel production using machine learning algorithms. His role involved data analysis, predictive modeling, and AI deployment in real-world environments. He also contributed to a deep learning-based search engine enhancement project for a pharmaceutical retail company. In addition, at FPT Information System’s Smart City Department, he developed camera-based systems for sidewalk encroachment detection, which were integrated into Ho Chi Minh City’s traffic management system. Currently, as a Graduate Research Assistant at Pukyong National University, he is involved in automating weld inspection systems and developing AI models for defect detection in scanning acoustic microscopy. His experience bridges academic research and practical industrial implementation.
Research Interest🔎
Nhan T. Nguyen’s research interests lie at the intersection of artificial intelligence, computer vision, and industrial automation, with a particular focus on low-level vision tasks and non-destructive testing. He is passionate about developing advanced AI models such as Generative Adversarial Networks (GANs), transformers, and diffusion models for applications in image restoration, super-resolution, and defect detection. His work emphasizes enhancing the performance and reliability of automated inspection systems used in semiconductor manufacturing, steel production, and other industrial settings. Nhan is also interested in integrating AI with robotic systems, using tools like 3D scanners, lasers, and cameras to automate surface inspection processes. Additionally, he explores exploratory data analysis across multiple domains, including medical, environmental, and industrial datasets. His goal is to bridge the gap between theoretical research and practical implementation, contributing to more intelligent, accurate, and efficient inspection and monitoring systems in smart manufacturing and biomedical engineering environments.
Award and Honor🏆
Nhan T. Nguyen has received numerous awards and honors in recognition of his academic excellence, innovative research, and technical achievements. He was awarded a scholarship by Pukyong National University in 2023 for his outstanding performance as a graduate student. During his undergraduate studies at Ho Chi Minh University of Technology, he received the prestigious KMS Technology Scholarship in 2022, as well as the City Now Company Scholarship and the Impressive Award in the HUTECT Start-up Wing competition in 2021. He also earned a Consolation Prize in the university’s AI Hackathon in 2020 and was recognized for his undergraduate research contributions. Nhan consistently demonstrated academic excellence, earning the Outstanding Undergraduate Student Scholarship in 2018. These honors reflect his dedication to research, creativity in problem-solving, and strong commitment to applying AI technologies to real-world challenges. His consistent recognition throughout his academic career underscores his potential as a leading researcher in his field.
Research Skill🔬
Nhan T. Nguyen possesses a robust set of research skills that span artificial intelligence, computer vision, and industrial automation. He is highly proficient in data processing, exploratory data analysis, and model development using Python and advanced machine learning frameworks. His expertise includes designing and implementing deep learning models, particularly using Generative Adversarial Networks (GANs), transformers, and diffusion models for image super-resolution, denoising, and defect detection. Nhan is skilled in integrating AI models with hardware systems such as robotic arms, 3D scanners, lasers, and industrial cameras to build intelligent inspection systems. He has hands-on experience with non-destructive testing methods, particularly scanning acoustic microscopy, and is adept at handling real-world industrial datasets. Additionally, Nhan is capable of deploying AI solutions into operational environments, enhancing automation processes in sectors like semiconductor manufacturing, smart cities, and healthcare. His ability to bridge theoretical models with practical applications showcases his strong technical and problem-solving capabilities as a researcher.
Conclusion💡
Nhan T. Nguyen demonstrates exceptional promise and proven capability in applied AI and biomedical inspection research, with practical impact, strong publications, and academic service. For a master’s-level researcher, this profile is outstanding.
Publications Top Noted✍️
📄 1. GAN-Based Super-Resolution in Linear R-SAM Imaging for Enhanced Non-Destructive Semiconductor Measurement
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Authors: Thi Thu Ha Vu, Tan Hung Vo, Trong Nhan Nguyen, Jaeyeop Choi, Le Hai Tran, Vu Hoang Minh Doan, Van Bang Nguyen, Wonjo Lee, Sudip Mondal, Junghwan Oh
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Year: 2025
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Citation (DOI): 10.3390/app15126780
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Source: Applied Sciences, Published on June 17, 2025
📄 2. Transformer-Based Super-Resolution Scanning Acoustic Imaging for Industrial Inspection
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Authors: Trong Nhan Nguyen, Vu Hoang Minh Doan, Tan Hung Vo, Jaeyeop Choi, Junghwan Oh
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Year: 2025
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Citation (DOI): 10.1109/icit63637.2025.10965207
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Source: 2025 IEEE International Conference on Industrial Technology (ICIT), Published on March 26, 2025
📄 3. Optimizing Scanning Acoustic Tomography Image Segmentation With Segment Anything Model for Semiconductor Devices
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Authors: Thi Thu Ha Vu, Tan Hung Vo, Trong Nhan Nguyen, Jaeyeop Choi, Sudip Mondal, Junghwan Oh
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Year: 2024
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Citation (DOI): 10.1109/TSM.2024.3444850
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Source: IEEE Transactions on Semiconductor Manufacturing, Published in November 2024
