Introduction

I am an Assistant Professor at the School of Artificial Intelligence and Data Science, specializing in medical imaging, deep learning, explainable AI, and trustworthy AI models for healthcare applications. I earned my Ph.D. in Computer Science and Engineering from IIT BHU, where I developed an AI-assisted diagnostic framework for cancer classification, integrating machine learning, deep learning, and interpretability techniques to enhance model reliability and clinical usability. My research in medical imaging spans cancer diagnosis, class imbalance handling, federated learning for medical AI, and multimodal diagnostics, with a strong emphasis on explainability, robustness, and real-world deployment. Beyond medical AI, my three-year postdoctoral research extended to AI-driven antimicrobial peptide (AMP) discovery as an alternative to antibiotics. In AMP discovery, I utilized deep learning and generative models to predict novel antimicrobial candidates, contributing to advancements in precision medicine and computational drug discovery. Additionally, my work on livestock identification focused on computer vision-based biometric authentication and disease monitoring, supporting precision livestock farming and veterinary diagnostics. I have contributed to high-impact publications, algorithmic advancements, and interdisciplinary collaborations across healthcare AI, computational biology, and animal biometrics. As a reviewer for top-tier IEEE Transactions journals, I actively contribute to advancing the frontiers of AI, machine learning, and biomedical informatics. My ongoing research focuses on developing explainable AI frameworks for reliable medical AI, learning with limited data, deep learning solutions for complex computer vision challenges and federated learning for privacy-preserving medical diagnostics.