Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro, Brazil
Topics
Error Control Coding Information Theory and Coding
Biography
Hassan Touati is a researcher and engineer specializing in telecommunications, machine learning, and signal processing. His work focuses on federated learning, semantic communications, distributed optimization, and advanced decoding techniques. He has strong expertise in gradient-based optimization methods, privacy-preserving communication systems, and reliability-aware learning frameworks.
Hassan has contributed to research on federated learning algorithms, including gradient correction mechanisms, aggregation strategies, and non-IID data modeling. His interests also extend to semantic communication systems enhanced by knowledge-driven models and efficient representation learning. He actively works on improving convergence performance and robustness in distributed learning environments.
With a solid background in mathematical modeling and algorithm development, Hassan implements and evaluates advanced machine learning architectures using Python and deep learning frameworks. His recent research includes privacy-preserving semantic communication through federated learning, LDPC decoding optimization, and performance analysis under heterogeneous data distributions.
Hassan is passionate about bridging theory and practical implementation, aiming to design efficient, scalable, and reliable communication-intelligent systems for next-generation networks.
