Published: 28 June 2013

Student wins international tech award

Liam Comerford

A PhD student at the University of Liverpool has won an award for the best student paper at the IEEE Symposium Series on Computational Intelligence (SSCI).

Liam Comerford, a PhD student from the Institute of Risk and Uncertainty and the School of Engineering, was recognised by a panel of distinguished international academics who reviewed those nominated from more than 650 papers in the conference.

The winning paper is a result of Liam’s multi-disciplinary work between engineering, mathematics and computer science. He is co-supervised by Dr Ioannis Kougioumtzoglou and Professor Michael Beer.

The prestigious conference, which was held in Singapore this year, is one of two flagship international events of the IEEE Computational Intelligence Society promoting all aspects of the theory and applications of computational intelligence.

Director of the Institute for Risk and Uncertainty at the University of Liverpool, Professor Michael Beer, joined Professor Vladik Kreinovich from UT EI Paso and Professor Rudolf Kruse from U Magdeburg in an international and multi-disciplinary collaboration to organise one of the largest symposia within the IEEE Symposium Series. Following the success of the event this year they have been invited to continue the activity at the IEEE SSCI in Orlando in 2014.

Commenting on Liam’s award win, Professor Michael Beer said: “It’s fantastic that Liam has won such a prestigious award. The number and quality of submissions for the IEEE are always high, so to be judged as the best is a real achievement. Our all-round PhD training in the risk institute is a real catalyst for career and leadership development and it’s great to be able to watch students move from strength to strength.”

Liam’s paper focuses on the risks associated with earthquakes, wind storms and sea storms, and proposes a technology that would enable a realistic risk analysis to be made based on incomplete or limited data records. His novel technology uses methodologies based on artificial neural networks in combination with a wavelet based approach from engineering.

The University’s Institute for Risk and Uncertainty offers cutting-edge expertise and methodologies to quantify, mitigate and manage risk and uncertainty across many fields. For more information please visit

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