In his research, Antonio strives to improve the effectiveness and accessibility of deep learning technologies in science and engineering by pioneering new architectures and training techniques grounded in theoretical knowledge. His work encompasses two main areas: understanding the intricacies of large-scale optimization dynamics and designing innovative architectures and powerful optimizers capable of handling complex data. Central to his studies is exploring innovative techniques for decoding patterns in complex sequential data, with implications spanning biology, neuroscience, natural language processing, and music generation.

deep learning optimization long-range reasoning deep learning theory