Dr. Andreas Damianou,
Research Associate, Institute for Translational Neuroscience, Robotics group, University of Sheffield
System identification and control with (deep) Gaussian processes

Thursday, Feb 11, 2016, 1:00pm to 2:00pm | Room 5-314

Work in Gaussian processes (GPs) is setting a new paradigm for data-driven modeling in engineering fields, such as control, dynamical systems and robotics. In control and systems identification, GP-based approaches often outperform traditional NAR(MA)X and Kalman filtering schemes. The attractive properties of GPs in these settings include their Bayesian, non-parametric nature and principled uncertainty quantification/propagation. In this talk I will give a brief introduction to non-parametric modelling with GPs and review work which applies them in the control and dynamical systems domain. I will then introduce recent, powerful approaches obtained by combining GPs with latent variable and deep learning techniques.

Short Bio:

Andreas Damianou is a research associate affiliated with the Institute for Translational Neuroscience and the Robotics group at the University of Sheffield. His area of expertise is Machine Learning with applications in robotics, computational biology and computer vision. He received his PhD from the University of Sheffield in 2015 under the supervision of Prof. Neil Lawrence, and his work on Deep Gaussian Processes and Variational Propagation of Uncertainty is the fist attempt to interface deep learning with Gaussian processes and Bayesian non-parametrics.