My research focuses on leveraging machine learning methods to model and control time continuous dynamical systems. Inspired by traditional scientific parametric model building, I am working on developing novel strategies for reinforcement learning, system identification and control.
As a doctoral fellow of the Max Planck ETH Center for Learning Systems, I am also a member of the Learning and Adaptive Systems group headed by Andreas Krause at ETH Zürich.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 89, pages: 1351-1360, (Editors: Kamalika Chaudhuri and Masashi Sugiyama), PMLR, April 2019 (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