Research InterestS

My research interests lie at the intersection of AI, computer vision, and data science, with a focus on machine learning and deep learning techniques applied to pattern recognition, data mining, and data clustering/classification. Over the years, my research has made contributions, securing funding and publishing in esteemed journals. Its impact extends beyond academia, having been utilized to tackle pressing issues in fields like digital humanities and remote sensing.

My research trajectory has predominantly centered on unsupervised machine learning, particularly in textured image analysis, encompassing tasks such as segmentation and denoising. Additionally, I have delved into document image classification and enhancement. I've also explored ensemble machine learning and deep learning methodologies, notably in the context of change detection in remote sensing imagery.

In recent endeavors, my research includes the Non-negative Matrix Factorization (NMF) paradigm, leveraging its capabilities for data representation, reduction, and clustering. Through this multifaceted approach, I aim to continue pushing the boundaries of knowledge in these domains, driving innovation and addressing real-world challenges.

My ongoing research is centered on Continual learning and Graph Neural Networks (GNNs), with a specific focus on their application to Alzheimer's disease detection.