As a PhD researcher at ÉTS, Amardip Kumar Singh collaborates with VMware on a cutting-edge project aimed at improving the training costs of Federated Learning (FL) models in Open Radio Access Network (O-RAN). His research considers key factors such as user mobility, network topology, and resource constraints. He has developed a novel optimization method, MCORANFed, which enhances communication efficiency by 13% and reduces resource usage costs by 19% compared to existing FL variants. Additionally, he has conducted convergence analysis and experimental validation of the method, presenting his work at the IEEE Wireless and Networking Conference 2022. Amardip's passion for data science and machine learning is rooted in a strong background in mathematics and edge computing. Holding a master's degree in computer science and technology from Jawaharlal Nehru University, he previously worked as a research fellow on computation resource management in edge and fog computing. He has developed expertise in statistical analysis, feature engineering, mathematical modeling, and data visualization using various tools and programming languages, including R, SQL, MATLAB, Python, and Tableau. To further strengthen his competencies, he has also obtained a Google Data Analytics Professional Certificate and a Mitacs Project and Time Management Certificate. Driven by the opportunity to apply theoretical knowledge and practical skills to real-world challenges, Amardip is committed to solving problems that create a positive societal impact. With a strong desire to contribute to both the research community and industry, he seeks to advance the state-of-the-art in FL, O-RAN, and data science.