Md. Rahad is a computer scientist specializing in federated learning, machine learning, and distributed artificial intelligence. He earned his Bachelor of Science degree in Computer Science from MBSTU, Bangladesh. Rahad is the first author of a peer-reviewed article published in a Q1 Neuroscience Informatics journal by Elsevier, presenting novel federated learning techniques using Kullback-Leibler divergence for handling non-IID data. His research interests focus on privacy-preserving AI, distributed optimization, and scalable federated systems with real-world applications. Rahad actively contributes to the academic community through peer reviewing for IEEE journals and aims to advance secure and efficient AI frameworks in decentralized data environments.