Eduardo Cueto is a researcher and data scientist specializing in machine learning, natural language processing, and computational mathematics. Currently pursuing a PhD at Technological University Dublin, his work focuses on optimizing the power efficiency of deep neural networks, addressing critical challenges in sustainable AI development. With experience as a Research Fellow, Tutor, and Data Analyst, Eduardo has contributed to innovative methodologies, impactful research, and the mentoring of future professionals in computer science.
Throughout his career, Eduardo has developed practical tools, such as Docker containers for real-time AI visualization, and authored research on neural network compression, entropy, and data compression. His work has been presented at international conferences and published in academic settings, bridging the gap between cutting-edge research and real-world applications.
Eduardo’s commitment to collaboration and societal impact is reflected in his leadership roles, public engagement efforts, and contributions to improving energy-efficient technologies and customer-centric AI solutions. As an advocate for open science, he fosters transparency and innovation within the research community while striving to make AI more accessible and sustainable.
