about

research statement

I am a researcher in reinforcement learning, a field of artificial intelligence that studies how agents learn by interacting with their environment. My current research focuses on principled ways of decomposing reinforcement learning problems into simpler tasks whose solutions can be combined to quickly solve the original problem (see this paper or this blog post for an overview).

short bio

I received my PhD in Computational Systems from Universidade Federal do Rio de Janeiro in 2008 (part of it was done in the Colorado State University). After that, I spent two and a half years as a postdoc in the Reasoning and Learning Laboratory at McGill University. In 2013, I became an assistant researcher in the Department of Applied and Computational Mathematics at the National Laboratory for Scientific Computing. In 2016 I joined DeepMind, where I am now a research scientist.  cv ↓