Nadeem Qazi
Senior Lecturer in AI machine learning at University of East London,UK
Alzheimer’s disease (AD) is becoming more common, emphasizing the need for early detection and prediction to improve patient outcomes. Current diagnostic methods are often too late, missing the opportunity for early intervention. This research seeks to develop advanced explainable AI models that combine various types of data, such as brain scans, genetic information, and health records, to identify early signs and risk factors of AD. By doing so, the project aims to enhance early diagnosis, reduce healthcare costs, and enable personalized treatments. By making the model’s predictions transparent and explainable, clinicians can understand the driving factors behind assessments, enhancing trust and patient communication. Additionally, explainable AI addresses ethical concerns related to bias, fairness, and accountability in automated decision-making.
Keywords: Explainable AI,Trustowrthy AI, Vision Transformers, Alzheimer diseases, deep learning, Transfer learning
Scientific area: Explainable AI, Deep Transfer Learning, multi modelling, Computer vision, Data fusion
Bio: Dr. Qazi is an accomplished researcher and Senior Lecturer at the University of East London, UK, specializing in Artificial Neural Networks (ANN) for controlling and predicting nonlinear processes, with a PhD from Cranfield University, UK. Currently serving as Principal Investigator and visiting researcher at the University of Eindhoven, he contributes to a project on Deepfake videos using Generative AI. At the University of East London, he supervises research on computer vision algorithms for cell aging prediction and leads an AI research group. Throughout his career, Dr. Qazi has led national, international, and EU-funded projects, focusing on AI, Explainable AI, Generative AI, Deep Learning, Transfer Learning, NLP, Text Mining, Big Data, and Visual Analytics. His research addresses diverse challenges in crime analysis, smart cities, human activity recognition, assisted living, energy monitoring, human-robot interface, and process monitoring in power plants. Noteworthy achievements include leading the UK-China collaborative IQuest project at Brunel University London, developing intelligent computer vision algorithms for smart homes. At Middlesex University, London, in the EU-funded VALCRI project, he pioneered an Intelligent Visual Analytic Framework for crime analysis, significantly advancing the understanding and visualization of crime patterns.
Visiting period: 20/06/2024-15/08/2024 at Tietoevry Finland