MacSeNet recruited 17 ESRs in 9 universities and 1 SME across 7 countries. We ran 3 training courses in transferable skills covering: Software Carpentry courses, research data management, data sharing, open publishing and science communication, on Entrepreneurship, IP and Licencing, communication to the media and the public, applying for jobs, interview techniques and applying for research funding. We ran a project-based sandpit where teams worked to solve a real world problem via Agile project management. We ran 2 science schools offering lectures and tutorials on scientific topics; both free for non-network researchers to attend. We held 2 Workshops, the first SPARS2017 and the second in Paris in March 2018.
Our Fellows have produced results across 5 research areas:
In Core Theories and Algorithms they have developed a number of new methods, including using approximations where trying to find exact solutions would be intractable; new efficient methods that are scalable to large problems; a special type of averaging for fast optimization algorithms that is better than traditional linear estimators; and speeding up algorithms by “compressing” large-scale problems into smaller problems, and exploiting the structure of the datasets.
In Advanced Brain Imaging and Analysis they have produced algorithms to: improve clinical diagnosis when using a reduced MRI scan time for patient comfort; improve the speed and results of the post-processing of MRI scans helping clinicians to better understand the scan results; improved the models when working with fMRI which measures the flow of blood in the brain so that scientists and doctors can see which areas of the brain are working; improved the way researchers can look at EEG and fMRI results in combination to understand how the brain works.
In Inverse Imaging Problems they have developed techniques to remove image noise from MRI scans, deblur images including noisy natural images and produce better noise models for all types of image denoising.
In Audio Machine Sensing they have studied how to remove non-linear noise such as clipping and quantisation, detect audio events from data with little training and separate the singer from the music on an old jazz track.
Going beyond traditional signals they have found a new, graph-based method of learning space-time signals which has been applied to natural language modelling and brain network analysis and a method for detecting anomalies in large graphs allowing for event detection using the dynamics of Web and social networks.
These results were published in 9 journal articles, 61 conference papers and 6 technical reports. They worked directly with industry to share their techniques with those who will use them, leading to a patent application on image denoising techniques, and they have presented their research to the wider public via outreach activities.
