My research interests lie at the intersection of bioinformatics, computational genomics, and public health informatics, with a strong emphasis on leveraging cutting-edge technologies such as Artificial Intelligence (AI), Machine Learning (ML), and High-Performance Computing (HPC). I am particularly focused on developing innovative AI/ML pipelines to enhance predictive modeling accuracy in the context of genomics and health data. My work includes streamlining data engineering processes, implementing scalable cloud solutions using AWS, and driving large-scale genomic data analysis projects. Additionally, I am passionate about advancing public health research through the application of text mining and natural language processing (NLP) to extract epidemiological insights from complex healthcare data, such as verbal autopsies and clinical reports. A significant part of my research is dedicated to improving the accuracy and efficiency of public health informatics systems, thus enabling better decision-making and resource allocation in healthcare settings. With extensive experience in mentoring students and securing research funding, I aim to contribute to the development of a more data-driven, sustainable, and accessible healthcare ecosystem.

Dr. M.T. Mapundu is an accomplished Bioinformatician and IT professional with vast expertise in teaching, supervision, bioinformatics, computational genomics, public health informatics, and IT systems management. Currently serving as a Research Fellow at Northumbria University, he specializes in AI/ML-driven bioinformatics research and high-performance computing for genomic data analysis. Dr. Mapundu has a proven track record in curriculum development, research supervision, and securing research funding, alongside delivering impactful lectures and workshops. His extensive academic and industry experience spans institutions in South Africa and the UK, with notable contributions to public health research through machine learning applications. He has published numerous research articles, with a focus on AI-driven health data analysis.