Computer Vision

In the field of Computer Vision, I am interested in enabling machines to interpret and understand visual information from images and videos. My work aims to develop models that can perform tasks such as object detection, image classification, segmentation, and scene understanding, contributing to applications in areas like healthcare, automation, and surveillance.

Deep Learning

My research in Deep Learning revolves around designing and training deep neural networks to automatically learn hierarchical representations from large datasets. I am especially focused on applying deep learning to solve challenging problems in image and video analysis, leveraging architectures like CNNs, RNNs, and transformers for improved accuracy and efficiency.

Data Mining

My research interest in Data Mining focuses on discovering meaningful patterns, trends, and insights from large and complex datasets. I aim to develop efficient algorithms and models that can extract hidden knowledge to support decision-making processes. This includes work on classification, clustering, association rule mining, anomaly detection, and pattern recognition, with applications in domains such as healthcare, education, and business intelligence.