PhD student at the Empirical Inference Department at the Max Planck Institute (MPI) for Intelligent systems (Tübingen). I'm interested in probabilistic machine learning, change point modeling and reinforcement learning. In particular, I am working with Bernhard Schölkopf on learning models of the world which are invariant across different domains, and using them to devise sample-efficient learning algorithms that can be ultimately applied in real settings (e.g. robot platforms). Before joining the MPI I did both my Bachelors and Masters at the Universidad Tecnológica de Pereira (Colombia) where I worked with Mauricio Alvarez in the probabilistic representation of movement primitives using Gaussian Processes.
Aside of my main research interests, I am also interested in different broad aspects of computing such as algorithm design and competitive programming.
Proceedings of the 36th International Conference on Uncertainty in Artificial Intelligence (UAI), 124, pages: 320-329, Proceedings of Machine Learning Research, (Editors: Jonas Peters and David Sontag), PMLR, August 2020 (conference)
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems