Meiwei Zhang | AI with Medical | Best Researcher Award

Published on by Innovation Technologist

Dr. Meiwei Zhang | AI with Medical | Best Researcher Award

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 🎓

  • Doctorate in Medical Information Processing
    Chongqing University, China (2021–2025)
    Focus: AI in Healthcare, Alzheimer’s Disease diagnosis using multimodal and neuropsychological data

  • MSc in Software Engineering
    Athlone Institute of Technology, Ireland (2019–2020)
    Focus: Applied machine learning and NLP systems development

  • BSc in Microelectronics
    University of Electronic Science and Technology of China (2011–2015)
    Foundation in electronics, computing, and data systems

Professional Experience 💼

  • 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.

  • NLP Intern – Huawei Ireland (2020)
    Designed knowledge graph-enhanced chatbot systems and optimized dialog management via slot-filling mechanisms.

  • NLP Engineer – Huatong Technology (2016–2019)
    Structured medical data using NLP and developed AI-powered Assistance Diagnostic Systems for hospitals.

  • System Engineer – IBM (2015–2016)
    Worked on POC development for enterprise systems and contributed to solution design and implementation.

Research Interests 🔬

  • Medical Information Processing

  • Alzheimer’s Disease Diagnosis

  • Multimodal Learning and Fusion Techniques

  • Natural Language Processing & Speech Recognition

  • Large Language Models (GPT-4, GPT-4o, RAG, CrewAI)

  • 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.

  • Notable achievement: Best Paper Award at IEEE IMSA 2024 for work on AI-based speech recognition in Chongqing dialect.

Awards and Honors🏆✨

  • Best Paper Award – IEEE IMSA 2024
    For AI-based Automatic Speech Recognition tailored to cognitive assessment of Chongqing dialect-speaking older adults

  • 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:

  • Robust research productivity in a highly relevant healthcare AI domain

  • Proven record in building practical, intelligent medical tools

  • Expertise in modern AI techniques including LLMs and multimodal learning

Publications 📚

  • 🧠 “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

  • 🧠 “A Multimodal Learning Machine Framework for Alzheimer’s Disease Diagnosis Based on Neuropsychological and Neuroimaging Data”
    📘 Computers & Industrial Engineering, 2024, 110625
    👥 Co-authors: Multiple

  • 🗣️ “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

  • 🏅 “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.

  • 🤖 “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

  • ⚠️ “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

  • 🧠 “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.

  • 🧠 “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.

  • 💬 “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

  • 🔤 “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

  • 🤖 “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

 

 

 

Hailong Yan | Energy Storage | Best Researcher Award

Published on by Innovation Technologist

Mr. Hailong Yan | Energy Storage | Best Researcher Award

Professor at Nanyang Normal University, China

Shahzeb Khan 🎓✨ is a passionate Data Scientist and Assistant Professor with a flair for innovation and research. With expertise in AI, ML, and data science, he blends academic rigor with practical problem-solving skills. He has been recognized for his research excellence, including the prestigious Best Researcher Award (2024), and holds qualifications like GATE 2020 and UGC-NET 2023. Beyond his professional pursuits, Shahzeb enjoys reading, writing, cooking, solo traveling, swimming, and singing. 🌍📖🎤

Publication Profile : 

Scopus

Educational Background 🎓

Shahzeb Khan is a dedicated Data Scientist and educator with a strong background in information systems analysis, programming, teaching, and research. He earned his M.Tech in Computer Science from Mahatma Gandhi Central University, Bihar, in 2023 with an impressive 8.5 CGPA, after completing his B.Tech from AKTU Uttar Pradesh in 2020 with a 6.8 CGPA. Shahzeb demonstrated academic excellence from an early age, achieving 9.2 CGPA in matriculation (CBSE) in 2013 and 75% in his intermediate studies in 2015.

Professional Experience 💼

Shahzeb is currently serving as an Assistant Professor in Computer Science and Applications at Sharda University, Greater Noida, where he teaches Artificial Intelligence, Machine Learning, Big Data Analytics, Data Science, and Data Analytics (since September 2023). His professional journey also includes a web development internship at IIY Software Pvt. Ltd. in 2019 and key coordination roles, such as the TSML (Time Series Machine Learning) Summer Training Program at MGCUB in 2023. Shahzeb is actively involved in conducting workshops and seminars, including those at IIT Delhi, where he engaged in IoT and Machine Learning training.

Research Interests 🔬

Shahzeb’s research interests lie at the intersection of Artificial Intelligence, Machine Learning, Deep Learning, and Statistical Modeling. He is particularly focused on predictive modeling, hybrid model development, and comparative analysis of advanced algorithms for time-series data. His notable work includes a publication on a novel Hybrid GRU-CNN model for PTB diagnostic ECG time series data in the Biomedical Signal Processing and Control journal, which boasts a 5.9 impact factor.

Publications 📚

  1. Khan, S. (2024). A Novel Hybrid GRU-CNN and Residual Bias (RB) Based RB-GRU-CNN Models for Prediction of PTB Diagnostic ECG Time Series Data. Biomedical Signal Processing and Control, Impact Factor: 5.9.

 

 

Shahzeb Khan | AI in Healthcare | Best Researcher Award

Published on by Innovation Technologist

Mr. Shahzeb Khan | AI in Healthcare | Best Researcher Award

Assistant professor at Sharda University, India

Shahzeb Khan 🎓✨ is a passionate Data Scientist and Assistant Professor with a flair for innovation and research. With expertise in AI, ML, and data science, he blends academic rigor with practical problem-solving skills. He has been recognized for his research excellence, including the prestigious Best Researcher Award (2024), and holds qualifications like GATE 2020 and UGC-NET 2023. Beyond his professional pursuits, Shahzeb enjoys reading, writing, cooking, solo traveling, swimming, and singing. 🌍📖🎤

Publication Profile : 

Google Scholar

Educational Background 🎓

Shahzeb Khan is a dedicated Data Scientist and educator with a strong background in information systems analysis, programming, teaching, and research. He earned his M.Tech in Computer Science from Mahatma Gandhi Central University, Bihar, in 2023 with an impressive 8.5 CGPA, after completing his B.Tech from AKTU Uttar Pradesh in 2020 with a 6.8 CGPA. Shahzeb demonstrated academic excellence from an early age, achieving 9.2 CGPA in matriculation (CBSE) in 2013 and 75% in his intermediate studies in 2015.

Professional Experience 💼

Shahzeb is currently serving as an Assistant Professor in Computer Science and Applications at Sharda University, Greater Noida, where he teaches Artificial Intelligence, Machine Learning, Big Data Analytics, Data Science, and Data Analytics (since September 2023). His professional journey also includes a web development internship at IIY Software Pvt. Ltd. in 2019 and key coordination roles, such as the TSML (Time Series Machine Learning) Summer Training Program at MGCUB in 2023. Shahzeb is actively involved in conducting workshops and seminars, including those at IIT Delhi, where he engaged in IoT and Machine Learning training.

Research Interests 🔬

Shahzeb’s research interests lie at the intersection of Artificial Intelligence, Machine Learning, Deep Learning, and Statistical Modeling. He is particularly focused on predictive modeling, hybrid model development, and comparative analysis of advanced algorithms for time-series data. His notable work includes a publication on a novel Hybrid GRU-CNN model for PTB diagnostic ECG time series data in the Biomedical Signal Processing and Control journal, which boasts a 5.9 impact factor.

Publications 📚

  1. Khan, S. (2024). A Novel Hybrid GRU-CNN and Residual Bias (RB) Based RB-GRU-CNN Models for Prediction of PTB Diagnostic ECG Time Series Data. Biomedical Signal Processing and Control, Impact Factor: 5.9.