Diretor da EPFL (École Polytechnique Fédérale de Lausanne) e politécnico, professor Ali H. Sayed dará palestra na Poli-USP nesta quarta-feira, 11 de outubro

Abstract:  Modern society is witnessing the emergence of complex networked systems driven by exchanges of information among their elements, such as robotic swarms, autonomous systems, social networks, and Internet-of-Things (IoT) architectures. In these applications, data is often collected from heterogeneous sources and from dispersed locations. It becomes imperative to design learning machines and decision-making algorithms that are better suited to the reality of networked units. The methodologies will need to account for coupling among intelligent agents in a manner that enables multi-tasking and is robust to interference. This talk presents a framework for decentralized learning from networked data, which we refer to as Social Machine Learning. This approach handles heterogeneity in data more gracefully than existing methods, learns with performance guarantees, is more resilient to adversarial attacks, and promotes explainable and fair learning. By relying on social interactions among agents, the architecture is also infused with a higher level of robustness to manipulation. This is because it is more difficult to fool a group of agents than an individual agent.

Bio: A. H. Sayed serves as Dean of Engineering at EPFL, Switzerland, where he also directs the Adaptive Systems Laboratory. He has served before as Distinguished Professor and Chair of Electrical Engineering at UCLA. He is a member of the US National Academy of Engineering (NAE) and The World Academy of Sciences (TWAS). He served as President of the IEEE Signal Processing Society in 2018 and 2019. An author of over 600 scholarly publications and 9 books, including most recently the 3-volume treatise on Inference and Learning from Data, published by Cambridge University Press in 2022. His research involves several areas including adaptation and learning theories, data and network sciences, statistical inference, and information processing theories. His work has been recognized with several major awards including more recently the 2022 IEEE Fourier Technical Field Award and the 2020 IEEE Wiener Society Award.