Artur Negrão is a accomplished Research Scientist at Google DeepMind, specializing in the theoretical foundations of machine learning and artificial intelligence. His research interests are deeply rooted in reinforcement learning, deep learning theory, optimization, and understanding generalization in AI models. Artur is dedicated to advancing the fundamental understanding of how complex learning algorithms work, with a focus on developing more robust, efficient, and reliable AI systems. His work often involves rigorous mathematical analysis and aims to bridge the gap between theoretical insights and practical applications, contributing to the cutting-edge advancements in the field of AI.
Artur Negrão's work history includes a series of influential roles in various companies. Here is a detailed list of his professional journey:
Authored and co-authored influential research papers in top-tier AI conferences (such as NeurIPS, ICML, ICLR) that advance the theoretical understanding of reinforcement learning algorithms, including their convergence properties, sample efficiency, and stability.
Conducted research providing key insights into why deep learning models generalize well, particularly in overparameterized regimes. His work explores the interplay between optimization algorithms, model architecture, implicit regularization, and data distributions.
Contributes to research initiatives focused on AI safety and the robustness of machine learning models, aiming to ensure that AI systems are reliable and behave as intended in complex and potentially adversarial environments.
Explored and developed novel optimization methods tailored for large-scale machine learning problems, contributing to more efficient training of deep neural networks and other complex models.
FAE Centro Universitário - Year 2015
FAE Centro Universitário - Year 2012
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