Speaker

Kim Phuc Tran is currently a Senior Associate Professor (Maître de Conférences HDR, equivalent to a UK Reader) of Artificial Intelligence and Data Science at the University of Lille, Graduate School MADIS-631, ENSAIT, & GEMTEX laboratory, France. He received an Engineer's degree and a Master of Engineering degree in Automated Manufacturing. He obtained a Ph.D. in Automation and Applied Informatics at the University of Nantes, and an HDR (Doctor of Science or Dr. Habil.) in Computer Science and Automation at the University of Lille, France. He has published more than 72 papers in peer-reviewed international journals and proceedings of international conferences. He edited 3 books with Springer Nature and Taylor & Francis. He is the Associate Editor, Editorial Board Member, and Guest Editor for several international journals such as IEEE Transactions on Intelligent Transportation Systems and Engineering Applications of Artificial Intelligence.

Kim Phuc Tran has supervised 12 Ph.D. students and 3 Postdocs. In addition, as the project coordinator (PI), he conducted a national project about Healthcare Systems with Federated Learning. He has been or is involved (PI, co-PI, or member) in 13 national and European projects. He is an expert and evaluator for the Public Service of Wallonia (SPW-EER), Belgium, the Natural Sciences and Engineering Research Council of Canada, ANRT (Association nationale de la recherche et de la technologie), and CY Cergy Paris University, France. He received the Award for Scientific Excellence (Prime d’Encadrement Doctoral et de Recherche) given by the Ministry of Higher Education, Research and Innovation, France for 4 years from 2021 to 2025 in recognition of his outstanding scientific achievements.

From 2017 until now, he has been the Senior Scientific Advisor at Dong A University and the International Research Institute for Artificial Intelligence and Data Science (IAD), Danang, Vietnam where he has held the International Chair in Data Science and Explainable Artificial Intelligence. His research interests include Explainable Trustworthy, and Transparent Artificial Intelligence; Ethical, and Human-centered Artificial Intelligence; Safety and Reliability of Artificial Intelligence; Statistical Computing; Intelligent Decision Support Systems; Digital Twins; and Applications of AI, Edge Computing, and Data Science in Industry 5.0.

Research Interests:

• Explainable Trustworthy, and Transparent Artificial Intelligence:  Self-Supervised Learning,  Anomaly Detection, Federated Learning, Federated Reinforcement Learning, Quantum Machine Learning, Inverse Reinforcement Learning
• Ethical and Human-centered Artificial Intelligence: Embedded AI, Wearable AI Devices, Human-Centered Design to Address Biases in AI, Augmented Intelligence,  Human-Robot relations and collaborations, Human Impact, Augment Human Capabilities, and Intelligence 
• Safety and Reliability of Artificial Intelligence: Adversarial Machine Learning, Detecting Poisoning Attacks, Cybersecurity for AI Systems, Blockchain Empowered Federated Learning, Consensus Protocol, Evolutionary Computing, Swarm Algorithms
• Statistical Computing: Statistical Process Monitoring,  Advanced Control Charts, Quality Control, Interpreting out-of-control signals using Machine Learning,  Control Chart Pattern Recognition (CVPR) with Machine Learning, Screening and Early Detection and Monitoring of Infectious Diseases
• Intelligent Decision Support Systems: Embedding domain knowledge for Machine Learning, Clinical Decision Support Systems, Supply Chain Optimization, Production Optimization, Demand Forecasting,  Cybersecurity for industrial control systems, Fault Detection and Diagnostics, Predictive Maintenance, Natural Language Processing for Fashion Trends Detection
• Digital Twins: Digital Twins in Healthcare, Digital Twin Application for Production Optimization, Digital Twin Drives Smart Manufacturing
• Applications of AI, Edge Computing, and Data Science in Industry 5.0: Digital Transition and AI, Twin Green and Digital Transition, AI for Health and Wellbeing, Smart Healthcare, Smart Manufacturing, Workplace Safety Wearables, Reliability Engineering, AI-aided Knowledge Discovery,  Sustainable Fashion.

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Title: Machine Learning and Control Chart for Anomaly Detection: Methods, Applications, and Challenges

Abstract: 
The recent development of advanced technologies such as Smart Sensor Networks, the Internet of Things (IoT), and Artificial Intelligence (AI) drive continuous improvement, knowledge transfer, and data-driven decision-making in many fields. The technique of Anomaly Detection (AD) supports decision-making for a large number of studies, to detect rare events or observations that deviate from normal behaviour. Applications of AD: Intrusion detection in a computer network, spotting potential risk or medical problems in health data, and predictive maintenance. Challenges that the current anomaly detection methods can address and envision this area from multiple different perspectives.