News

There will be an Open Day for potential PhD and OxWaSP applicants on Wednesday 25th November.

The event will commence at 2pm in the Statistics Common Room (Zeeman Building).

Details of the agenda for the day can be found by following this link.

The ground-breaking work of Dr Ben Graham, a WDSI member who works in Statistics and Complexity, is featured in a recent Economist article (Now There's an App for That, 19 September 2015) about the success of machine-learning approaches to rapid diagnosis of a common disease from retinal images.

Ben's work made him the global winner of a recent Kaggle competition on this machine-diagnosis problem. (This was not Ben's first such success: In 2013 he was the winner of a similar competition organised by ICDAR, with a novel method for recognition of online Chinese handwriting.)

To explain in a bit more detail what he did, Ben writes:

Kaggle obtained pairs of retinal images from Eyepacs/the California Healthcare Foundation from about 44,000 people at risk of diabetic retinopathy. Each of the 88,000 images was graded by a human expert on a scale from 0 to 4; most of the images were healthy zeros. Elevated scores indicate a risk of vision loss. The scores were made available for about 18,000 people to form a training set, with the rest of the scores kept secret to form a test set. The images varied substantially in quality, from in-focus 3000x3000 pixel images to images that were completely blank. Most of the images were of fairly high quality.

I trained a convolutional neural network (or three) to classify the images. To try and boost accuracy, I used the classification for each left-right pair of images to produce the final per-eye classifications, combining scores with a random forest.

The quadratically weighted kappa agreement between my program and the human graders was 0.850. The quadratically weighted kappa agreement between the first and second place computer programs was 0.933; substantially higher. There are two possible explanations:

1. The computers are making systematic errors, and totally missing information available to the human graders. This would be the case if the training set is too small to contain a full range of relevant symptoms.
2. The human graders make mistakes, and the computers have learnt to classify the images more accurately that humans.

The truth is likely to be a mix of the two.

The example images below are of a healthy retina (first image) and a high-risk case.

Many congratulations to Pieralberto Guarniero upon winning a Warwick Award for Teaching Excellence for Postgraduates Who Teach (WATE PGR). The WATE PGR programme seeks to recognise the best teachers from Warwick's postgraduate community, and Pieralberto 's enthusiasm and dedication to the subject were recognised as key factors behind his success in this years awards.

A link to the WATE PGR webpage, which includes a full list of this year's winners as well as a statement on Pieralberto 's achievements, can be found here.

Professor Jane Hutton has been appointed with effect from 1st November 2015 as a UCU-nominated director of USS - the Universities Superannuation Scheme.

Professor Hutton has been one of the leaders in substantial efforts to challenge the large changes to USS which are in train as a consequence of the latest actuarial valuation, and hopes to continue to ameliorate the effects of those changes. The USS has assets of about 42 billion pounds.

There are 12 directors: four appointed by UUK, three appointed by UCU and five independent directors.