Chongqing University, Ireland
Dr. Meiwei Zhang is a multidisciplinary researcher with a strong academic and industrial background in medical information processing, natural language processing (NLP), and artificial intelligence applications in healthcare, particularly Alzheimer’s Disease diagnosis. With significant experience in both academic research and industry-led AI development, Dr. Zhang has contributed to numerous peer-reviewed publications in high-impact journals, focusing on the integration of multimodal data, speech recognition, and large language models (LLMs). His diverse educational background, coupled with applied experience in industry (PWC, Huawei, IBM), demonstrates a robust alignment between theory and practice.
Publication Profile
Scopus
Educational Background 🎓
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Doctorate in Medical Information Processing
Chongqing University, China (2021–2025)
Focus: AI in Healthcare, Alzheimer’s Disease diagnosis using multimodal and neuropsychological data
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MSc in Software Engineering
Athlone Institute of Technology, Ireland (2019–2020)
Focus: Applied machine learning and NLP systems development
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BSc in Microelectronics
University of Electronic Science and Technology of China (2011–2015)
Foundation in electronics, computing, and data systems
Professional Experience 💼
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Data Scientist – PWC AC (2022–2024)
Developed applications using LLMs for report analysis, claims processing, and ESG assessments. Built RAG-based conversational AI systems using LangChain, FAISS, Azure Cognitive Search, and Llama-Index.
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NLP Intern – Huawei Ireland (2020)
Designed knowledge graph-enhanced chatbot systems and optimized dialog management via slot-filling mechanisms.
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NLP Engineer – Huatong Technology (2016–2019)
Structured medical data using NLP and developed AI-powered Assistance Diagnostic Systems for hospitals.
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System Engineer – IBM (2015–2016)
Worked on POC development for enterprise systems and contributed to solution design and implementation.
Research Interests 🔬
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Medical Information Processing
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Alzheimer’s Disease Diagnosis
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Multimodal Learning and Fusion Techniques
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Natural Language Processing & Speech Recognition
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Large Language Models (GPT-4, GPT-4o, RAG, CrewAI)
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Cognitive Frailty Prediction & Elderly Health AI Systems
Publications & Academic Contributions
Dr. Zhang has authored/co-authored over 10 high-quality publications, including in Computers in Biology and Medicine, BMJ Open, and the Journal of Alzheimer’s Disease. His research includes multimodal machine learning frameworks, dialectal speech recognition for elderly cognitive screening, and self-learning AI agent systems for medical diagnostics.
Awards and Honors🏆✨
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Best Paper Award – IEEE IMSA 2024
For AI-based Automatic Speech Recognition tailored to cognitive assessment of Chongqing dialect-speaking older adults
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Multiple Publications Under Review – in top-tier journals including Information Fusion and Journal of the American Medical Directors Association
Conclusion🌟
Dr. Meiwei Zhang demonstrates strong suitability for recognition such as a Best Researcher Award. His work represents a unique and impactful intersection of medical science and artificial intelligence, particularly in the under-researched domain of Alzheimer’s diagnosis in diverse linguistic populations. His career reflects an excellent balance of academic achievement, technical skill, and real-world application.
He stands out in the following ways:
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Robust research productivity in a highly relevant healthcare AI domain
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Proven record in building practical, intelligent medical tools
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Expertise in modern AI techniques including LLMs and multimodal learning
Recommendation: Highly recommended for competitive research awards in AI in Medicine, Bioinformatics, or Applied Machine Learning in Healthcare.
Publications 📚
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🧠 “A Feature-Aware Multimodal Framework with Auto-Fusion for Alzheimer’s Disease Diagnosis”
📘 Computers in Biology and Medicine, Vol. 178, 2024, 108740
👥 Co-authors: Q. Cui, Y. Lü, W. Li
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🧠 “A Multimodal Learning Machine Framework for Alzheimer’s Disease Diagnosis Based on Neuropsychological and Neuroimaging Data”
📘 Computers & Industrial Engineering, 2024, 110625
👥 Co-authors: Multiple
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🗣️ “Augmented Dialectal Speech Recognition for AI-Based Neuropsychological Scale Assessment in Alzheimer’s Disease”
📘 Biomedical Signal Processing and Control, Vol. 99, 2025, 106821
👥 Co-authors: Q. Cui, W. Li, W. Yu, L. Chen, Y. Lü
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🏅 “An AI-Based Automatic Speech Recognition for Chongqing Dialect Older Adults with Cognitive Impairment”
📘 IEEE IMSA Conference, 2024 (🏆 Best Paper Award)
👥 Co-authors: W. Li, L. Chen, Q. Cui, J. Yu, et al.
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🤖 “An LLM-Based Self-Learning and Critical Agent Framework for Multimodal Alzheimer’s Disease Diagnosis”
📄 Information Fusion – (Under Review)
👥 Co-authors: Q. Cui, Y. Lü, Y. Pan, W. Yu, W. Li
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⚠️ “A Risk Prediction Model for Falls Among Older Adults Inpatient with Cognitive Frailty: Machine Learning Study Based on Comprehensive Geriatric Assessment”
📄 Journal of the American Medical Directors Association – (Under Review)
👥 Co-authors: Lihua Chen#, M. Zhang#, W. Yu, X. Liu, Y. Lü
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🧠 “A Fully Automated Mini-Mental State Examination Assessment Model Using Computer Algorithms for Cognitive Screening”
📘 Journal of Alzheimer’s Disease, 2024, pp. 1–12
👥 Co-authors: L. Chen, W. Yu, J. Yu, Q. Cui, et al.
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🧠 “Differentiating Alzheimer’s Disease from Mild Cognitive Impairment: A Quick Screening Tool Based on Machine Learning”
📘 BMJ Open, Vol. 13, No. 12, 2023: e073011
👥 Co-authors: W. Lü, W. Yu, L. Chen, et al.
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💬 “CoURAGE: A Framework to Evaluate RAG Systems”
📘 Proceedings of the 29th International Conference on NLDB, 2024
👥 Co-authors: D. Galla, S. Hoda, W. Quan, T. Yang, J. Voyles
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🔤 “Smooth Embedding and Word Sampling Research Based on Transformer Pointer Generation Network”
📘 International Journal of Machine Learning and Computing, Vol. 11, No. 3, 2021
👥 Co-authors: Y. Wu, Y. Pang, H. Wen, J. Wang
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🤖 “Improved Multi-Round Chatbot using Data Augmentations and Loss Regularization”
📘 ICMAI 2020 – 5th International Conference on Mathematics and AI, 2020
👥 Co-authors: Y. Wu, X. Ji, J. Gao