Permanent Director of IAD, Head of Chair at International Chair in Data Science and XAI and Lecturer at Dong A University

Assist. Prof. Kim Duc TRAN

Assist. Prof. Kim Duc TRAN is an interdisciplinary researcher, academic leader, and educator specializing in Artificial Intelligence, Data Science, Statistical Process Monitoring, Explainable Artificial Intelligence, Edge AI, Smart Manufacturing, and AI-enabled Healthcare. He currently serves as Permanent Director of the International Research Institute for Artificial Intelligence and Data Science (IAD) and as a Permanent Lecturer and Researcher at Dong A University, Da Nang, Vietnam. In parallel, he is a Postdoctoral Researcher within the SYMPHONIES project at GEMTEX, ENSAIT, University of Lille, France, where his research focuses on artificial intelligence, digital twins, signal processing, medical compression textiles, and intelligent data-driven systems.

Since 2018, Dr. Tran has played a central role in developing the International Chair in Data Science and Explainable Artificial Intelligence at Dong A University. He served as Local Chair from 2018 and subsequently assumed the position of Head of the Chair from December 2025. His academic responsibilities also include serving as Scientific Director at Dong A University, coordinating international academic projects, participating in academic recruitment and selection committees, chairing student research juries, and contributing to the development of engineering curricula in Artificial Intelligence, Integrated Circuits, advanced mathematics, probability and statistics, Python programming, and algorithms.

Dr. Tran completed his Ph.D. in Computer Science and Automation at the University of Lille, France, Graduate School MADIS-631, in close collaboration with GEMTEX and ENSAIT. His doctoral research, entitled “Contributions to Monitoring, Anomaly Detection, and Classification Using Artificial Intelligence and Statistical Methods: Applications in Smart Manufacturing and Healthcare,” investigates advanced methods for intelligent monitoring, anomaly detection, classification, reliability analysis, and decision support across industrial and healthcare systems.

His doctoral work lies at the intersection of Industry 4.0 and Industry 5.0, combining Artificial Intelligence, Internet of Things, cloud and edge computing, statistical process monitoring, federated learning, explainable AI, and reliability engineering. A major objective of his research is to move beyond purely automated systems toward human-centered, sustainable, resilient, trustworthy, and interpretable intelligent systems.

His doctoral research is structured around three interconnected scientific directions. The first concerns adaptive, privacy-preserving anomaly detection and classification for smart healthcare, including federated learning and explainable AI approaches that support decentralized, real-time analysis of sensitive healthcare data. The second focuses on AI-driven anomaly detection, predictive maintenance, and prognostics for sustainable smart manufacturing, integrating machine learning, federated learning, blockchain, and intelligent monitoring technologies to improve security, fairness, transparency, efficiency, and industrial resilience. The third research direction develops statistical computing, industrial statistics, control charts, reliability analysis, and data-driven quality monitoring techniques for industrial process monitoring and decision-making.

Before completing his doctoral training, Dr. Tran developed an unusually broad multidisciplinary educational background. He received an Engineer’s degree in Civil Engineering, specializing in Hydroelectricity and Irrigation with a minor in Construction Informatics, from the University of Science and Technology – The University of Da Nang in 2011. His engineering research focused on construction informatics for hydroelectric projects, with his graduation project addressing the design and construction of the Song Tranh 2 Hydropower Dam.

He subsequently completed a Bachelor of Business Administration at Duy Tan University, where his research focused on Supply Chain Management and Data Science, particularly distribution channel management in the petroleum and asphalt sector. He later obtained a Master of Business Administration from Duy Tan University in 2022, with research focusing on Data Science in Customer Relationship Management. This combination of engineering, business administration, computer science, statistics, and artificial intelligence has shaped a strongly interdisciplinary approach to his scientific work.

Dr. Tran also brings substantial industrial and software-development experience to his academic research. From 2010 to 2018, he served as Director of Kien Phuc Co., Ltd., working in automation, electrical and mechanical engineering and providing technical solutions for industrial production systems. During this period, he led the design, implementation, integration, and upgrading of automated production-control systems incorporating real-time monitoring, IoT-based data acquisition, custom control algorithms, industrial automation, and data-driven optimization.

His subsequent research has extended this practical engineering experience toward modern Industry 5.0 software and intelligent-system architectures. His technical expertise includes Python programming, machine learning, deep learning, federated learning, lightweight transformer architectures, explainable artificial intelligence, Internet of Things time-series analytics, cloud–edge hybrid systems, intelligent digital twins, predictive maintenance, anomaly detection, privacy-preserving decentralized AI, and scalable software solutions for smart factories.

He has applied full software development lifecycle principles across multidisciplinary research projects, including requirements analysis, data acquisition, data pipeline engineering, model design and training, algorithm development, deployment, testing, version control, validation, and the integration of intelligent systems into industrial environments.

A significant part of Dr. Tran’s recent research has been conducted within major French and international collaborative projects. As an AI Research Engineer in the REVALOTEX project (2023–2026) at GEMTEX/ENSAIT, he contributes AI expertise to research on the transformation of the textile industry in line with circular-economy principles, including textile product value recovery, sustainability, intelligent characterization, and lifecycle extension.

Within the SYMPHONIES project, a large-scale research and innovation initiative dedicated to advanced medical textile compression solutions for lymphoedema, he works as a Postdoctoral Researcher specializing in AI-driven digital twins, signal processing, smart-textile data analysis, and intelligent medical-compression systems.

Dr. Tran has also served as Principal Investigator or Co-Principal Investigator on research initiatives associated with the International Research Institute for Artificial Intelligence and Data Science. These include the International Chair in Data Science and Explainable Artificial Intelligence (XAIDS_IChair, 2018–2028), the project “A New Framework for Prognostics in Decentralized Industries: Enhancing Fairness, Security, and Transparency through Blockchain and Federated Learning,” and the project “Predictive Maintenance Optimization Based on Genetic Algorithms for Future Industrial Systems.”

He has additionally contributed to the Smart Healthcare System with Federated Learning (SHSFL) project, supported through I-SITE ULNE, as well as the REVALOTEX and SYMPHONIES collaborative research programs. Collectively, these activities connect fundamental AI and statistical research with applications in smart healthcare, sustainable manufacturing, medical textiles, circular economy, predictive maintenance, and decentralized intelligent systems.

Research Interests

Dr. Tran’s current research interests include:

Statistical and Machine Learning methods for anomaly detection in multivariate and non-stationary time series
Statistical Process Monitoring and Statistical Quality Control
Control charts, reliability analysis, and short-run production monitoring
Artificial Intelligence and Machine Learning for stock price and financial data prediction
Explainable Artificial Intelligence and Responsible AI
Human-Centered Artificial Intelligence
Federated Learning and privacy-preserving AI
Edge Artificial Intelligence and Edge Computing
Digital Twins and intelligent cyber-physical systems
Artificial Intelligence for Industry 4.0 and Industry 5.0
Smart Manufacturing and Predictive Maintenance
AI-aided Knowledge Discovery
Reliability, Safety, and Industrial Decision Support
Artificial Intelligence for Health and Wellbeing
Smart Healthcare and personalized healthcare
Medical textiles and intelligent compression systems
Wearable technologies for workplace health and safety
Twin Green and Digital Transition
Sustainable Manufacturing and Sustainable Fashion
Circular-economy applications of Artificial Intelligence
Multimodal Artificial Intelligence for textile characterization and recycling
IoT-based monitoring and multivariate sensor analytics
Trustworthy, Transparent, Ethical, and Human-Centered AI
Hardware Security and Bit-Flipping Attacks

Teaching and scientific training constitute another important dimension of Dr. Tran’s academic activities. At Dong A University, he has designed and delivered courses on Machine Learning with Python and on the application of ChatGPT and generative AI to optimize student learning and scientific research. His AI-assisted learning and research activities have reached more than 3,200 students across Information Technology, Automotive Engineering, Civil Engineering and Technology, Electrical Engineering, Accounting and Finance, and other academic disciplines. He has also provided Machine Learning with Python training for master ’s-level students participating in international academic programs.

In addition to teaching, he actively supervises and mentors international engineering students. His CV records supervision of more than 22 ENSAIT–University of Lille engineering students participating in international semester projects at Dong A University. Their projects have addressed topics including generative AI in fashion, responsible AI for textile supply chains, personalized fashion recommendation, diabetes prediction using machine learning, fashion-item recognition using deep learning, and customer segmentation.

His doctoral-level collaborative activities extend to research on IoT multivariate time-series anomaly detection, secure and privacy-preserving federated learning for smart healthcare, 3D human leg morphology classification for medical compression stockings, Edge AI for smart factories, and explainable anomaly detection using deep reinforcement learning and lightweight federated learning.

Dr. Tran is also actively involved in academic governance and scientific evaluation. He has served as an expert and evaluator for Dong A University’s Research and Innovation Program and for scholarship programs supporting excellent lecturers and researchers associated with the International Chair in Data Science and Explainable Artificial Intelligence. He has participated in selection committees for university lecturer appointments in Electrical and Electronic Engineering and has contributed to academic program development in AI and Integrated Circuits.

His scholarly service includes extensive peer-review activities for leading international journals in artificial intelligence, engineering, operations research, industrial statistics, reliability, transportation, manufacturing, and sustainability. Journals for which he has reviewed manuscripts include Engineering Applications of Artificial Intelligence, IEEE Transactions on Intelligent Transportation Systems, Computers & Industrial Engineering, European Journal of Operational Research, Information Processing & Management, Information Sciences, International Journal of Industrial Ergonomics, International Journal of Production Research, Journal of Process Control, Measurement, Reliability Engineering & System Safety, Chemometrics and Intelligent Laboratory Systems, Quality Technology & Quantitative Management, Results in Engineering, Sustainable Futures, Next Sustainability, and Journal of Mathematics in Industry, among others. His CV records nearly 100 individual manuscript review assignments across these outlets.

Dr. Tran is equally active in organizing international scientific events. His roles have included Local Chair of the International Workshop on Responsible Artificial Intelligence and Applications in Integrated Circuit Industry, Manufacturing, and Healthcare, technical-program responsibilities for the EAI International Conference on Safety and Security in Internet of Things (SaSeIoT), Local Arrangement Chair for the event Explainable Artificial Intelligence for Industry 5.0, member of the organizing committee of the ISSAT International Conference on Data Science in Business, Finance and Industry, participation in the technical program of the International Congress on Advanced Technologies (ICAT 2025), and membership of the organizing committee of the EAI International Conference on Responsible Artificial Intelligence and Data Science (RAIDS 2026).

His research is supported by a broad international network of collaborators spanning academics and research institutions in France, Belgium, Canada, the United Kingdom, Germany, Italy, Malaysia, Greece, and Vietnam. Collaborators listed in his academic portfolio are affiliated with institutions including the University of Lille, ENSAIT/GEMTEX, HEC Management School–University of Liège, École Polytechnique de Montréal, University of Salford, McMaster University, University of Angers, Université Polytechnique Hauts-de-France, Centrale Lille Institut, Universiti Sains Malaysia, University of Catania, University of Rennes, Harz University of Applied Sciences, Ghent University, UTBM–FEMTO-ST/CNRS, University of Reims Champagne-Ardenne, Cardiff Metropolitan University, University of Piraeus, University of the Aegean, Heriot-Watt University Malaysia, Hanoi University of Science and Technology, and Honda Research Institute Europe.

Dr. Tran has authored and co-authored more than 30 international scientific publications, including peer-reviewed journal articles, book chapters published by Springer and CRC Press, refereed international conference papers, and invited presentations. His research has appeared in, or been associated with, internationally recognized journals and publishers such as Computers & Industrial Engineering, European Journal of Operational Research, Journal of Manufacturing Processes, Applied Mathematical Modeling, International Journal of Production Research, Quality and Reliability Engineering International, Intelligent Systems with Applications, Springer Nature, CRC Press, and IEEE.

His publications span several major research areas: statistical process control; monitoring of ratios of normally distributed variables; measurement-error effects in control charts; compositional-data monitoring; predictive maintenance; explainable anomaly detection; transformer architectures; federated learning; IoT cybersecurity; smart healthcare; ECG monitoring; wearable technologies; smart supply-chain management; digital twins; medical compression stockings; human-centered Edge AI; physics-informed machine learning; sustainable manufacturing; and Responsible AI.

His recent research increasingly focuses on the convergence of Human-Centered AI, Explainable AI, statistical monitoring, smart textiles, sustainable manufacturing, healthcare, and Industry 5.0. This includes work on reliable smart textiles, separating physiological anomalies from sensor drift and degradation, pressure prediction for medical compression stockings, anomaly detection in 3D human-leg morphologies, Edge AI for industrial fault diagnosis, multimodal AI for textile recycling, and adaptive anomaly detection for non-stationary multivariate time series.

Dr. Tran has also delivered invited scientific presentations on topics such as wearable technology for workplace safety in smart manufacturing, Explainable Artificial Intelligence for Healthcare 5.0, and transformer-based anomaly detection for ECG monitoring systems.

His academic and scientific contributions have been recognized with Scientific Excellence awards from Dong A University and Da Nang City, as well as earlier provincial awards for academic excellence. He also holds a Certificate of Pedagogical Professional Training for University and College Lecturers, supporting his combined roles as researcher, educator, academic leader, and scientific mentor.

Through a combination of engineering practice, industrial automation, computer science, business administration, statistical science, Artificial Intelligence, and international academic collaboration, Dr. Tran has developed a research profile focused on translating advanced AI methods into reliable, meaningful solutions for real-world systems. His long-term scientific objective is to contribute to the development of intelligent systems that are not only accurate and computationally efficient but also explainable, trustworthy, privacy-aware, sustainable, resilient, and human-centered.

His work particularly seeks to bridge theoretical developments in Artificial Intelligence and statistical learning with deployable solutions for smart manufacturing, healthcare, sustainable textiles, digital transformation, industrial reliability, workplace safety, and Industry 5.0.