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Assoc. Prof. Dr. Pham Tien Lam is a distinguished researcher and Lecturer at Phenikaa University, with deep expertise in computational materials science and AI-based materials informatics. He has held multiple research appointments in Japan, including at the Japan Advanced Institute of Science and Technology and the Institute for Solid State Physics, University of Tokyo. His academic work spans defect energetics, orbital interactions, and neural network models for materials property prediction.

Dr. Lam is the Principal Investigator for several national and international AI-focused materials projects funded by NAFOSTED and VKIST. He also serves as an AI consultant for smart technology enterprises and previously worked as a big data consultant for the World Bank. He has authored more than 15 publications in high-impact journals including IUCrJ, Cell Reports Physical Science, and Computational Materials Science.

Dr. Dang Thi Thuy An is a Lecturer and Researcher at Phenikaa University, bringing international experience in AI and signal processing from her postdoctoral work at Academia Sinica and doctoral studies in Taiwan. Her academic interests lie in acoustic scene classification, speech emotion recognition, and multimedia content understanding using deep learning.

Before joining Phenikaa University, she held academic positions at Danang University and worked in the software industry at Gameloft and FPT Software. Her publication portfolio includes papers presented at ICASSP and ACM Multimedia, reflecting her contributions to deep learning applications in real-time systems and acoustic intelligence. Dr. An actively serves as a reviewer for ICASSP, CSoNet, and CITA conferences and contributes to curriculum development at Phenikaa.

Dr. Do Quoc Truong is a Lecturer at Phenikaa University, with a research focus on speech and language processing technologies. He is the founder and CEO of Vietnam AI Solutions JSC (VAIS), where he leads product development for ASR and TTS systems in Vietnamese.

With international experience at Edinburgh University and NAIST Japan, Dr. Truong brings a multidisciplinary approach to neural language models, inverse text normalization, and ASR/NER integration. His academic output includes top-tier conference presentations at ICASSP, Interspeech, and O-COCOSDA. He is an active reviewer for conferences such as KSE and CSoNet and contributes to bridging academia and industry in AI-powered speech technologies.

Dr. Nguyen Ngoc Giang is a Lecturer and Researcher at Phenikaa University since May 2025. He brings extensive experience in AI and bioinformatics, acquired through research roles at Zenkei Corp (Japan) and prior lectureships at DaiNam University and Hanoi National University of Education. His research bridges deep learning applications in biomedical data, EEG signal classification, and behavioral analysis in animal models.

Dr. Giang has co-authored over a dozen publications in peer-reviewed journals and international conferences, including the Journal of Biomedical Science and Engineering, Applied Sciences, and the International Conference on Bioinformatics Models, Methods and Algorithms. He is an active reviewer for AI-related journals and continues to pursue professional development via AI-integrated teaching practices and online learning platforms.

M.Sc. Nguyen Van Son is a Lecturer at Phenikaa University, appointed in 2024, with a research background in computer science focused on wireless sensor networks, Internet of Things (IoT), and optimization techniques. He previously worked as an AI Specialist at VPS Securities and an R&D Engineer at Viettel Hightech, blending academic research with industrial application.

His academic contributions center around heuristic optimization, genetic algorithms, and network coverage in sensor systems. He actively contributes to the scholarly community as a reviewer for international journals and conferences including SUSCOM and IEEE CEC. M.Sc. Son is also involved in Ministry of Education-funded projects, particularly in IoT fault tolerance using multi-coverage techniques.