Uncertainty Quantification in Dynamical Systems
Alessandro Davide Ialongo (Ph.D. Student)
Many real-world systems are not static, they evolve through time. Modelling them as dynamical systems enables us to correctly account for non-stationarity and is a natural choice for sequential datasets. Especially in the low data regime, correctly quantifying predictive uncertainty is crucial to ensure we do not take overly optimistic decisions. In my PhD I have been developing techniques to apply Gaussian processes to sequential data and am now interested in applying these methods to tackle challenges in robotics. The aim is to achieve robust and data efficient robotic locomotion and manipulation.
|Primary Host:||Carl Edward Rasmussen (University of Cambridge)|
|Exchange Host:||Bernhard Schölkopf (Max Planck Institute for Intelligent Systems)|
|PhD Duration:||01 October 2016 - 31 May 2021|
|Exchange Duration:||01 November 2018 - 30 April 2020|