His area of research focuses on connecting language and vision for better interpretation of multidimensional data and spans over three broad areas: Machine Learning, Computer Vision, and Natural Language Processing with special emphasis on healthcare and use of natural language to explain the rationale and decision-making process behind the use of machine learning algorithms and models. With a strong background in computer vision and deep learning techniques, he has developed expertise in analysing and interpreting medical images (US, mammogram, CT, PET, MRI, fMRI) to improve patient outcomes. Throughout his career, he has worked on a range of projects that involve developing and implementing machine learning algorithms for medical image analysis. His work has resulted in more than 200 publications citation in leading journals and conferences in the field of medical image analysis. Through his research, he has made significant contributions to the field of medical image analysis, particularly in the area of early diagnosis. Additionally, he has collaborated with medical professionals to develop tools for early diagnosis of various medical conditions, including cancer, neurodegenerative disease, and heart disease.