Details of Grant 

EPSRC Reference:	EP/T013265/1
Title:	NSF-EPSRC:ShiRAS. Towards Safe and Reliable Autonomy in Sensor Driven Systems.
Principal Investigator:	Mihaylova, Professor LS
Other Investigators:
Researcher Co-Investigators:
Project Partners:	QinetiQ Rowan University Valerann UK Ltd
Department:	Automatic Control and Systems Eng
Organisation:	University of Sheffield
Scheme:	Standard Research - NR1
Starts:	01 December 2019	Ends:	30 November 2023	Value (£):	220,246
EPSRC Research Topic Classifications:	Artificial Intelligence
EPSRC Industrial Sector Classifications:	Information Technologies
Related Grants:
Panel History:
Summary on Grant Application Form
Modern data-driven algorithms trained over enormous datasets have revolutionised contemporary autonomous systems with their accurate predictive power. However, due to technical limitations, it is a challenge to integrate large-scale data from many different and complex sensors. Capturing the confidence of these algorithms also remains a challenge. In response to this demand, ShiRAS will develop pioneering approaches that will introduce autonomy at different levels in sensor-driven systems. The main focus is on machine learning methods with quantified uncertainty of the provided solutions. Within the field of machine learning, deep learning approaches have resulted in the state-of-the-art accuracy in visual object detection, speech recognition and translation, and many other domains. Deep learning can discover intricate structure in large data sets by using multiple levels of representation, where each level is a higher, more abstract representation of the data. However, a rigorous mathematical framework for uncertainty propagation and update in machine learning models has been largely underexplored. Most current deep learning techniques process the raw data in a deterministic way and do not capture model confidence or trust. Uncertainty can emanate from the noise in the raw data and the parameters of the approach and this impact is a critical part for any predictive system's output. By representing the unknown parameters using distributions instead of point estimates and propagating these distributions from the input to the output of the system, we propose promising machine learning methods able to handle uncertainty in a unified way.
Key Findings
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Organisation Website:	http://www.shef.ac.uk