The award for Student IT Innovation is advertised widely across the University and in the student press. In the past winners have been students who have developed various apps for mobile phones or to solve a technical problem they have encountered in their studies. This year our winners again show creativity, originality, impact and sustainability. This year’s entries are judged by last years winners.
First prize went to Mihran Vardanyan (Department of Physics and Christ Church) for his development of the iCosmos cosmology calculator. This innovative web-application allows researchers, educators and learners to compute different cosmological quantities and visualise a graphical representation of these quantities with a selection of cosmological parameters. iCosmos is being actively used in Cosmology research all over the globe, and is referenced in many Masters and PhD thesis’ to crosscheck results and compute theoretical values. Using a combination of advanced web technologies, the web app provides an efficient and speedy user experience, an invaluable tool for those in the field of Astrophysics and Cosmology.
Two runners up have also been awarded in this category. LHSee, developed by Chris Boddy (Department of Physics and Brasenose College), is a Smart Phone App that visualises collisions from the Large Hadron Collider, the world’s biggest scientific experiment. Users of the app can find out about more about the Large Hadron Collider, learn how the ATLAS experiment works, view live 3D displays of collisions direct from CERN, and play the ‘Hunt the Higgs’ game. Making an astonishing and complex process accessible to everyone the App is a novel way of attracting people into the world of Partical Physics.
Joshua Chauvin (Department of Experimental Psychology and New College) showed originality in his use of applied artificial neural networks to aid in the classification of children affected with Autism Spectrum disorders. ‘Neural Network Classification of Syndromic Facial Dysmorphology: Autism Spectrum Disorders’ collected image data from participants using a 3D photogammetric device to compile a facial image database of children unaffected with ASD. This was then compared with a facial image database of children diagnosed with ASD. The ANN exhibited strong predictive capabilities, suggestive of differences in facial morphologies. The success of this study provides evidence to support the hypothesis that there are differences in facial morphologies between children affected with ASD and children unaffected, and that ANNs are capable of recognizing these differences. Ongoing research is being carried out to further examine the potential clinical application of such computational models that has the potential to span philosophical, psychological, neurological, medical and social disciplines.