Bruno Castro da Silva

Bruno Castro da Silva is a postdoctoral associate at the Aerospace Controls Laboratory, at MIT. He received his Ph.D. in Computer Science from the University of Massachusetts, working under the supervision of Prof. Andrew Barto, in 2014. Before that he received a B.S. cum laude degree in Computer Science from the Federal University of Rio Grande do Sul in 2004, and a MSc. degree from the same university in 2007. Bruno has worked, in different occasions from 2011 to 2013, as a visiting researcher at the Laboratory of Computational Embodied Neuroscience, in Rome, Italy, developing novel control algorithms for the iCub robot. He has also worked at Adobe Research, where he developed novel large-scale machine learning optimization techniques for the construction of high-performance features for digital marketing optimization. His research interests lie in the intersection of machine learning, reinforcement learning, optimal control theory, and robotics, and include the construction of reusable motor skills, active learning, efficient exploration of large state-spaces and Bayesian optimization applied to control.

Bruno Castro da Silva is a professor at the Institute of Informatics of the Federal University of Rio Grande do Sul (UFRGS), in Brazil. Prior to that he was a postdoctoral associate at the Aerospace Controls Laboratory, at MIT. He received his Ph.D. in Computer Science from the University of Massachusetts, working under the supervision of Prof. Andrew Barto, in 2014. Before that he received a B.S. cum laude degree in Computer Science from the Federal University of Rio Grande do Sul in 2004, and a MSc. degree from the same university in 2007. Bruno has worked, in different occasions from 2011 to 2013, as a visiting researcher at the Laboratory of Computational Embodied Neuroscience, in Rome, Italy, developing novel control algorithms for the iCub robot. He has also worked at Adobe Research, where he developed novel large-scale machine learning optimization techniques for the construction of high-performance features for digital marketing optimization. His research interests lie in the intersection of machine learning, reinforcement learning, optimal control theory, and robotics, and include the construction of reusable motor skills, active learning, efficient exploration of large state-spaces and Bayesian optimization applied to control.