Luckeciano is a DPhil student in the OATML group, supervised by Yarin Gal and Alessandro Abate. His research interests lie in designing agents that learn behaviors through interactions in an efficient, generalist, safe, and adaptive way. He believes that such agents emerge from three main pillars: semantically rich representations of entities in the world; self-supervised World Models with inductive biases for memory and counterfactual reasoning; and fast policy adaptation mechanisms for out-of-distribution generalization.

Previously, he worked as an Applied Scientist at Microsoft, working with multi-modal representation learning and large language models for web data semantic understanding. He also led the RL Research group at the Center of Excellence in AI in Brazil, working with real-world RL applications in scalable digital platforms with industry partners.